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Changing commuters’ behavior using rewards: A study of rush-hour avoidance

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... e fare adjustment method can be classified into incentive policy and direct adjustment of fares. An investigation by Ben-Elia and Ettcema [1,2] showed that incentive measures effectively changed the travel time of passengers, where a few passengers opted to travel during off-peak hours. Zhang et al. [3] proposed an incentive policy to provide measures such as price reduction in fast-food restaurants for travelers who avoid traveling during peak periods. ...
... We define the departure time of the first bus as 6 a.m., based on the passenger flow information provided by the AFC. Passengers on trains 12-31 were selected as the research objects in this study (hereinafter collectively referred to as trains [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. MAT-LAB programming calculations were used to obtain the Mathematical Problems in Engineering convergence curve, as shown in Figure 4. ...
... Table 5 shows the subway fare table for the 9th train. e OD pair (1,11) in the table is 4/6.1, which represents the price before the price adjustment. e fare was 4 yuan, and the adjusted fare was 6.1 yuan. ...
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In urban rail transit, adjusting fares to satisfy passenger flow requirements is a new method to relieve urban congestion. A bilevel model is proposed herein to solve the congestion problem for an urban rail line. The upper level of the model determines the discount factor to minimize the total number of passengers exceeding the full-load rate, and the lower level of the model determines the distribution of passengers on the line, in which the cost-minimizing behavior of each passenger is considered using the allocation method based on the probability of selection. To achieve a more realistic model, the range of acceptable train numbers for each passenger is considered. A simulated annealing algorithm is introduced to solve the bilevel model. Based on an example, we obtain the specific fare and passenger flow distribution of each train after fare adjustment. The results show that the objective function is reduced by 17.5%, the congested section is reduced by 9.1% when the full-load rate is 90% of the train loading capacity, and the passenger flow shifts to both ends of the peak period. Finally, relevant parameters are discussed.
... Travelers' travel responses to peak avoidance policies are complex and influenced by various external factors (such as incentives) and travelers' own attributes (such as commuting and social demographic characteristics). Previous studies on travelers' peak avoidance decisions have generally assumed that the sensitivity of travelers to different factors is homogeneous (Ben-Elia & Ettema, 2011a, 2011bZhang et al., 2014;Wang et al., 2018). Consequently, such models result in 'one size fits all' policy recommendations. ...
... Modeling traveler responses to peak avoidance policies has been an important focus in these studies. Conventional choice models, such as the multivariate logit model (Ben-Elia & Ettema, 2011a, 2011b, multivariate probit model (Zhang et al., 2014), and binary logit model (Halvorsen et al., 2019) have been used to maximize the utility of choosing peak avoidance by incorporating individual socioeconomic characteristics and mode attributes (Ben-Akiva et al., 1999). However, the naturalistic data sources are typically anonymous and do not have information about the sociodemographic characteristics of the travelers. ...
... Therefore, field experiments can yield more accurate revealed preference data to inform policy evaluation. In recent years, we also have witnessed a rise in transportation research that uses a field experiment approach (Fujii and Kitamura 2003;Mazureck and van Hattem 2006;Ben-Elia and Ettema 2011). ...
... In theory, bicycle users who receive interventions to park in an orderly fashion may develop the habit of doing so in the long run-or they may tire of the measures, rendering the impact less effective over time. Studies have also shown a "boomerang effect" of monetary incentives, by which people lose intrinsic motivation once the monetary reward scheme ends Kitamura 2003, Ben-Elia andEttema 2011). Understanding the measures' long-term impact will be critical for their practicality when deployed in the field. ...
... Current practices for passenger behaviour change, such as policing and fines provide negative motivation in the form of a penalty as widely described in transport user studies (Bates et al., 2012;Perry et al., 2002;Chen et al., 2020). However, considerable evidence demonstrated that individuals were better motivated by rewards than punishments (Ben--Elia and Ettema, 2011;Geller, 1989). Nevertheless, there are several limitations of rewards. ...
... Thus, the influence solely elicited from the incentives may be limited. Furthermore, the long-term effects of incentives were questionable as individuals' behaviour induced by the incentives tended to return to the previous level when the incentives were no longer in place (Ben-Elia and Ettema, 2011;Fujii, Gärling, and Kitamura, 2001;Fujii and Kitamura, 2003;Thøgersen and Møller, 2008). ...
Article
Passengers' travel behaviour is one of the significant factors affecting train overcrowding. Train occupancy information has been introduced as a tool to stimulate passengers' behaviour change to ease in-vehicle crowding. However, there are limitations to this strategy as it often fails to consider other elements in the complex rail system that influence behaviour. This research provides insights to service providers to promote passenger behaviour change by revealing the behavioural constraints in the environment. Cognitive Work Analysis (CWA) was applied to systematically analyse passengers’ behaviour and related constraints in the environment. Specifically, Work Domain Analysis (WDA) and Social Organisation and Cooperation Analysis (SOCA) were conducted and presented in the forms of Abstraction Hierarchy (AH) and Contextual Activity Template (CAT). Results showed that a wide range of informational, navigational and physical support alongside provision of occupancy information could better encourage passengers to select and use less busy carriages and trains. Behaviour change goals are likely to be achieved more effectively when the constraints of the system are better understood.
... It is important to note that interaction with participants requires confidentiality of the experimental intention throughout the experiment, careful explanation of the rules, and avoidance of suggestive terminology to prevent the subject from judging whether the behavior is right or wrong before the experiment, thereby affecting the true performance in subsequent behavioral experiments [45]. Therefore, we only explained the rules before the experiment, and paid attention to the design of the pre-test to avoid direct inquiries about the shared-bicycle credit mechanism. ...
... No matter how they say that they do not care about the supervision system and "did not feel the stress at all", the users' unconscious behavior will show their true responds: they will decrease their abuse behavior under the carefully designed credit system. Moreover, the reason why it is after the formal experiment is that this questionnaire about user's preference should be conducted after the experiment to avoid emitting the experimental intention, therefore the result will be unbiased [45]. ...
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As a new travel model, the bike-sharing system (BSS) solves the ‘last kilometer’ problem and has developed rapidly for its convenience. However, many accompanying problems have emerged. In China, parking violation problems—such as severe traffic congestion—are caused by dock-less shared bikes. Furthermore, a large number of shared bikes have to be scrapped early for vandalism. As a special form of public good, bike-sharing also faces the dilemma of negative externalities. Seeking a solution, Mobike has conducted a credit supervision mechanism, which transfers the users’ different behavior to credits for user behavior regulation, but with unsatisfactory results. The goal of the paper is to test the validity of credit supervision mechanism from user’s perspective to regulate the abuse of sharing bike by simulating the use scenario of BSS in real life in a lab experiment based on induced value theory. The behavioral and pre- and post-experiment survey data were thoroughly analyzed. The results show that, within a negative context, the credit supervision system has a more significant effect on inducing proper user behavior, which improves after adding a real-time feedback mechanism. Finally, we provide effective suggestions to policy makers and shared bike companies for inducing positive user behavior.
... As opposed to road pricing that has been proved efficient yet controversial [19], the effectiveness of incentives to reinforce a desirable behavior is supported by a large volume of empirical evidence [20,21]. However, the implementation and relevant research on incentives in the transportation area has a relatively short history. ...
... Tripzoom, an app from Germany, builds in the ability for participants to set goals [26]. In the Netherlands, rewards are well-recognized as the extrinsic motivation for discouraging rush-hour driving, in which experiment a smartphone called 'Yeti' that worths 500€ is provided as reward, and an individual can gains some credits ranging from 3€ to 7€ depending on the time of a day [19,27]. In the US, Metropia, a new technology company, also explored to use a combination of incentives and information to change people's behavior of multiple dimensions, such as improving people's driving behavior for the purpose of reducing driving crashes [28]. ...
Article
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The uncertainty and unpredictability of a transportation network roots in the dynamics of individual travel behavior, which can be revised consciously or repeated habitually depending upon the reality and personality. In this paper, we propose to study the day-to-day departure time choice behavior of the travelers, using real observation data collected from a smartphone app, “Metropia”. Influenced by the information and incentives provided in the app and the comparison with the experience gained from the last trip, a transformation process of traveler’s day-to-day experience on departure time from an existing habit to a new one is analyzed in this study. The analysis result in a binary choice model for the shift of departure time for each repeated morning commute trip comparing with the last one, which proves that users’ experience in app engagement, previous travel time saving, habitual travel time, incentives, and commute flexibility are able to trigger day-to-day behavioral change for their morning home-to work commutes. The findings of this research provide insights on the users’ adaption to a new traffic information service along with incentives, and corresponding behavior changes over a transition period. The outcomes suggest ways to improve ICT services and incentive scheme, as well as the transportation operation and demand management.
... A personalized set of incentives (mostly monetary) is proposed in [10], where a platform is introduced that enables the commuters to receive incentives if they change their departure time to off-peak hours or use an alternative. Several other pilot studies have been performed and they have experimentally validated the benefits of soft policies, see [11] among others. It is important to stress that these two classes of measures are not always mutually exclusive but they can be used in combination to amplify the final effect of congestion alleviation, as we advocate in this paper. ...
... S.1) HO collects information: The HO collects information, from the sensors on the highway (placed at the interfaces between cells), on the cells' density, i.e., ρ (k) for all ∈ N . The HO computes the following set of variables: ξ(t) via (8), ϑ old (t), δ old (t), u old (t), ∆ (k) ,∆ (t) via (11), p(k) via (10) andp(t) via (12), by exploiting the CTM and the strategies of the PEVs that performed the process during the previous time intervals. ...
Preprint
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In this paper, we study how to alleviate highway traffic congestion by encouraging plug-in hybrid and electric vehicles to stop at a charging station around peak congestion times. Specifically, we design a pricing policy to make the charging price dynamic and dependent on the traffic congestion, predicted via the cell transmission model, and the availability of charging spots. Furthermore, we develop a novel framework to model how this policy affects the drivers' decisions by formulating a mixed-integer potential game. Technically, we introduce the concept of "road-to-station" (r2s) and "station-to-road" (s2r) flows, and show that the selfish actions of the drivers converge to charging schedules that are individually optimal in the sense of Nash. In the second part of this work, submitted as a separate paper (Part II: Case Study), we validate the proposed strategy on a simulated highway stretch between The Hague and Rotterdam, in The Netherlands.
... One possible approach for tackling rising congestion is to completely challenge traditional work practices and human mobility (Ben-Elia and Ettema, 2011;Hopkins and McKay, 2014;Lyonette et al., 2013;Waters, 2007). In the past, our tools of labour were responsible for dictating the location where workers needed to perform their tasks but now, in the digital era, many of our tools are portable and work tasks can be carried out anywhere. ...
Article
Despite the many potential economic, social and environmental benefits, the adoption rates for anywhere working in Australia remain very low. This explorative study aims to gain a deeper understanding as to why this is, by examining the working arrangements and commuting habits of a sample of employees from Melbourne's largest city-based firms, in order to identify current organisational policies relating to anywhere working, commuter transport modes/usage/timings, attitudes toward anywhere working, the percentage of time employees spent engaged in anywhere working, the location(s) where they typically performed anywhere working, and the benefits, constraints/concerns, perceived productivity, and equipment needed to effectively work in a location outside of a traditional office space. These findings offer a valuable new insight into this phenomenon, as a potential mechanism for reducing traffic in our cities of the future, by leveraging ICT technologies to reduce the overall need for people movement.
... Incentive-based TDM is recently receiving a lot of attention, and a number of studies (mainly related to traffic) point to the potential of incentives to impact travel behavior. Work schedules, household and work related constraints, and personal traits are important factors impacting the response to reward based TDM [10][11][12][13]. ...
Conference Paper
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Increasing ridership in metro systems is outpacing its capacity. Promotion based transit demand management can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. While several studies address short-term behavioral response to such promotions using before and after analysis, how behavioral changes are maintained in the long run is also very important. Using an extensive automated dataset over two years from the Hong Kong Metro system, this paper explores the longitudinal behavior of passengers in response to a promotion to shift their travel time to the pre-peak period. The approach uses customer segmentation to evaluate the response of different groups. The results highlight the heterogenous response of different groups. Users with high schedule flexibility, less variable itineraries of a trip and relatively long distances are more likely to shift their travel times. The longitudinal promotion analysis reveals that 35-40% of passengers who initially shifted will eventually revert to their previous travel time periods. Based on the results of the analysis, an 'optimal' promotion design approach is applied to examine the effectiveness of promotion strategies given different response assumptions, and constraints on budget and performance requirements. The promotion design using group-specific response can better target price-sensitive users, hence improves its effectiveness, while the design based on the long-term response shows a significant performance decrease.
... There are four predictors that include schedule delay early, schedule delay late, travel time saving, and the amount of reward points. The separation of delay early and delay late is needed because it has been found that people have different preferences on departing earlier or later if they are asked to change their travel plan [41]. As the number of canonical models is unknown, we apply the same cross-validation procedure used in simulation studies to determine K for LogCM and LogSCM. ...
Article
Understanding user behavior is crucial for the success of many emerging applications that aim to provide personalized services for target users, such as many patient-centered health apps and transportation apps. Models based on the random utility maximization (RUM) theory are widely used in learning and understanding behavioral preferences on the population level but find difficult to estimate individuals' preferences, particularly when individuals' data are limited and fragmented. To address this problem, our framework builds on the concepts such as canonical structure and membership vectors invented in recent works on collaborative learning and is suitable for modeling heterogeneous population with insufficient data from each individual. We further propose an extension of the collaborative learning framework using pairwise-fusion regularization as a knowledge discovery tool for real-world applications where the canonical structure is uneven, e.g., some canonical models may only represent minor subpopulations. Computationally competent algorithms are developed to solve the corresponding optimization challenges. Extensive simulation studies and a real-world application in smart transportation demand management (TDM) show the effectiveness of our proposed methods.
... Incentive schemes are the most common method adopted to influence people's travel behavior. Ben-Elia and Ettema [4] mentioned that incentives are effective tools in changing commuting behavior and can reduce the number of drivers during rush-hour, shift driving to off-peak times, and increase the usage of public transport. This study also examined how extra incentive rewards could increase carpooling usage. ...
Article
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The purpose of this study was to offer a comprehensive econometrical framework based on a multilevel random effect logistic model that could highlight important contributors to carpool users among different cities with various attributes. The data was collected from the three cities of Tucson, AZ, USA; El Paso, TX, USA; and Austin, TX, USA and was based on register-based travel trip data from the Metropia platform and American Community Survey information from 2016 to 2017. The empirical results indicated there were statistically significant differences among carpool users in different cities due to the transportation mode, number of vehicles available, total number of males driving alone, and number of single-parent households. The individual level result showed that incentives had a significant effect on the promotion of carpool passenger and driver behavior. In addition, the time of finding the parking space at work, living situation of the household, flexibility to change departure times, gender, and age could effectively increase the possibility of carpool usage. The results of this study give a better understanding of the events in the initial factors of carpooling behavior and can be used by the government or commercial company to design an effective solution for traffic congestion.
... © 2021 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology car use towards more sustainable travel modes, for example, the work of Brög et al. [2], Sanjust et al. [3], Ben-Elia and Ettema [4], and Lachapelle [5]. Such schemes work better than fiscal measures in the sense that they do not encourage socioeconomic inequity [6]. ...
Article
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Reduction of car use is one of the most effective ways to tackle congestion-related problems. Using positive incentives to stimulate bicycle use is one possibility to reduce car use. Cycling is a sustainable transport mode that uses little space and is healthy. There is evidence that positive incentives may be more effective than punishing travellers for undesirable behaviour, and the emergence of mobile applications for delivering interventions has opened up new opportunities for influencing travellers. So far, few studies have focused on exploring the effectiveness of positive incentives on long-term behavioural change. We used the SMART app to deliver positive incentives to more than 6000 travellers in the Dutch region of Twente. The app automatically tracks users and provides incentives such as challenges with rewards, feedback, and messages. This study covers the period from March 2017 to June 2018, in which more than 1000 SMART users participated in monthly challenges. We evaluated the effects of the challenges and rewards and found that the challenges did encourage cycling and reduced car use in the short term. There is also some evidence for behavioural change over a longer time period. © 2021 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
... A series of publications details incentives' effects on commuting patterns in the Spitsmidjen (peak avoidance) project in the Netherlands. Ben-Elia and Ettema ( , 2011, and Ettema, Knockaert, and Verhoef (2010) found that economic incentives were successful in changing commuting patterns by encouraging drivers to avoid commuting to and from work during peak hours (50-60% decrease). Their findings demonstrated that incentives can induce short-term modifications in commuter behaviour; however, once the incentive was discontinued, drivers returned to their former activity patterns. ...
... Some of the most common TFPs include individualised marketing where only participants who are keen to change their travel behaviours are provided personalised travel information (see Cairns et al., 2004) and travel blending, where participants receive booklets describing why an individual's travel behaviour is important (see Taniguchi, Hara, Takano, Kagaya, and Fujii, 2003). Other researchers used financial incentives to promote the use of sustainable modes (e.g., Ben-Elia and Ettema, 2011;Jakobsson et al., 2002;Kristal and Whillans, 2020). Other than education-and financial-based interventions, Travel Demand Management (TDM) has also been used. ...
Article
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Car use is a common travel mode in many societies but it has negative impacts on the environment and public health. There have been various interventions to reduce car use but self-persuasion has not been tested as a potential intervention. Self-persuasion involves asking people to generate arguments in favour of a specific issue. Our goal was to investigate the effectiveness of self-persuasion in changing drivers' car use attitudes and behaviours. A sample of New Zealand drivers (n = 183) completed two online questionnaires; one immediately after and one at least 2 weeks after the intervention. We randomly assigned the drivers to one of three conditions: self-persuasion (generating arguments on the benefits of reducing car use), direct-persuasion (read-ing arguments on the benefits of reducing car use), and control (completing a different travel-related task). There were no significant differences between the three groups of drivers on car use intentions for commuting trips, weekly car use for commuting and non-commuting trips, or attitudes towards reducing car use. We attributed the ineffectiveness of self-persuasion to the average quality of arguments generated, the effortful nature of reducing car use, and the COVID-19 situation in New Zealand. Although self-persuasion may not be an appropriate intervention in the travel behaviour domain, future research needs to continue identifying new ways to reduce car use to reduce its detrimental effects.
... Attention has recently been turned towards exploring the potential of approaches supporting behavioral change, including behavior feedback, social comparison, goal setting, gamification, as well as personalized suggestions and rewards (de Kruijf et al., 2018;Yen et al., 2018;Weber et al., 2018;Stark et al., 2018;Lieberoth et al., 2018;Anagnostopoulou et al., 2018;Bothos et al., 2014;Ben-Elia & Ettema, 2009). Within the last category, the vast majority of the transportation literature comprises papers that examine rewards offered to people with the aim of avoiding rush-hour travelling (Kumar et al, 2018;Yang & Tang, 2018;Khademi et al. 2014;Zhang et al., 2014;Nie & Yin, 2013;Knockaert et al., 2012;Ben-Elia & Ettema, 2011a, 2001bBakens et al., 2010;Merugu et al., 2009). Recently, a growing body of literature has focused on reward schemes and other persuasive strategies to encourage the shift towards public transport or non-motorized modes, while the rise of smartphone applications within the transport sector has created a relatively new era of research which examines the potential role of smartphone technology as a tool for promoting sustainable transport modes via the aforementioned schemes/strategies. ...
Article
Reward-based instruments have the potential to encourage individuals’ shift towards multimodal mobility options, thus contributing to a more sustainable and resilient transport environment. This paper aims to investigate the effects of reward-based instruments on promoting emerging mobility schemes and active transport, through real-world demonstrations in two European cities. Specifically, a route planning mobile application which tracks users’ travel patterns was used to integrate a reward program offering points to incentivize people towards sustainable multimodal choices, including public transport, cycling and walking. In addition, a web-based questionnaire survey was conducted, and a discrete choice model was developed to model individuals’ multimodal choice in the presence of different reward types, including monetary rewards, points and the provision of added value services. Overall, our findings indicate that reward-based instruments can contribute to the promotion of sustainable and emerging transport services. In particular, participants spent more time in public transport usage and walking during the reward-based period. Our results indicate that rewards could increase individuals’ time spent in public transport usage and walking by about 21 min and 14 min per day respectively. Furthermore, it is found that public transport users were mostly motivated by rewards, while car users and walkers were not motivated towards cycling. Finally, the results indicate that Birmingham’s users were more motivated than Vienna’s participants, as public transport usage increased by about 209 min per week in Birmingham vs. 74 min per week in Vienna. Similar patterns of increase in the cities were observed for walking, while some population groups in Vienna were found insensitive to the prospect of earning rewards for using sustainable transport modes.
... erefore, in this study, BI is used to predict actual behavior. Although TPB has strong universality in practice, it cannot fully explain the actual behavior under any circumstances, and there are some missing factors [30][31][32]; that is, in addition to the above factors, the BI may still be affected by some other undiscovered hidden factors. ...
Article
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The implementation of shared parking program can effectively increase the utilization rate of existing parking space resources. At present, shared parking program has not been widely practiced in China, and the prerequisite for this prospect to be implemented is whether the private parking space owner group can quickly and widely accept shared parking program. In this study, considering the differences in the economic development, urban planning, and parking pressure in cities of different levels, the theory of planned behavior and the benefit-risk perception model (C-TPB-BRA) are combined as the theoretical framework to explore the intention to share parking space from the perspective of the owners of private parking spaces in cities of different levels. Based on China’s empirical data, structural equation models are built to verify the hypotheses proposed. Our results show that (a) the intention of private parking space owners in different levels of cities to participate in shared parking and the mechanism of action of the psychological factors are different, and not all psychological factors have a direct impact on the intention to share. In first-tier, second-tier, and third-tier cities, Subjective Norm (SN) and Perceived Behavioral Control (PBC) indirectly affect Behavior Intention (BI) through Attitude (ATT), Perceived Benefit (PB), and Perceived Risk (PR). In the fourth-tier cities, SN and PBC directly affect BI. Except for BI, other psychological factors influence each other significantly; (b) the psychological factors affecting the intention to supply shared parking spaces in first-tier, second-tier, third-tier, and fourth-tier cities, respectively, are PB > ATT > PR, PB > PR > ATT, PB > PR > ATT, and PB > SN > PBC > ATT > PR. Our research results could help determine the internal factors that affect the intention of parking space suppliers and their mechanisms of action to participate in shared parking, and on that basis, our findings could also help governments and platform operators to promote shared parking development plans. 1. Introduction In recent years, the economy has developed rapidly and the number of motor vehicles has increased greatly, but the infrastructure construction and management level have not been correspondingly improved, so the contradiction between supply and demand of parking has become increasingly prominent. In addition, according to relevant statistics [1], in the context of sudden public health incidents such as the COVID-19, citizens are more concerned about the hygiene of public transportation and shared bicycles, resulting in a further reduction in the proportion of public transport trips, and the proportion of private car trips increased, which will further intensify parking needs. In addition, Amott [2] pointed out that, in Boston and some major European cities, more than 50% of cars need to find parking spaces during peak hours. The research of Shoup [3] pointed out that if each parking activity requires three minutes to find a parking spot, the cruising mileage of each vehicle needs to be increased by about 1825 kilometers per year. Besides, the lack of parking spaces can also lead to illegal parking activity, increased time costs caused by queuing and waiting, etc., thereby exacerbating carbon dioxide emissions [4]. This situation is more serious in many cities in China. Zhao et al. [5] constructed a quantitative model to evaluate the emission reduction effect of the implementation of the shared parking policy. The results show that 120 shared parking spaces in Beijing can reduce about 400 tons of carbon dioxide emissions a year; if 20% of the existing parking spaces in Beijing are shared, every year carbon dioxide emissions can be reduced by up to 7.3 million tons. Ayala [6] found that more than 3.1 million gallons of gasoline was wasted and more than 48,000 tons of carbon dioxide was emitted due to the search for parking spaces in Chicago. Therefore, if the problem of parking difficulty can be solved, the parking pressure can be effectively alleviated, the driver’s time to find parking is greatly reduced, and the environmental pollution caused by vehicle emissions can be alleviated. In addition to the contradiction between supply and demand of parking, another prominent manifestation of the current urban parking problem is the inefficient utilization of parking resources, which is mainly reflected in the imbalance in the space-time utilization of parking resources. For example, parking spaces in office areas are usually vacant at night and on weekends, while parking spaces in residential areas are often vacant during workdays during the day, which also provides an opportunity to meet parking demand without the need to build more parking lots [7]. According to relevant statistics, 485,000 parking spaces in Hong Kong are designated for private use, accounting for nearly 70% of the total number of parking spaces; Beijing’s residential parking resources account for 58.1% of all parking resources, during working hours nearly 800,000 private parking spaces have been left unused [8], and because most urban residents work inconsistently with their homes, parking spaces in residential areas have been unused during the day on weekdays. If the spare time of these private parking spaces can be used effectively, the parking problem can be greatly alleviated. In recent years, the concept of shared parking has been proposed, the basic idea of which is that the parking space owner sells parking permits for the idle period of their parking spaces to public users on the electronic parking platform [9], and travelers with parking needs can purchase a parking permit through the parking platform. The relationship between supply and demand is shown in Figure 1. Some cities have already experimented with shared parking, but private parking space sharing in residential areas is still in its infancy. Most urban residents do not know much about shared parking in residential areas, and the number of users of each shared parking platform is small, so participation in shared parking is far from enough. Therefore, it is very important for urban planning and parking management to understand the decision-making mechanism for people to accept shared parking.
... The results show that sociodemographic variables, family structure variables, personal work-related attributes, and travel characteristics all have a significant impact on departure time. Ben-Elia [12][13][14] found flexible working time, incentives and road tolls had an impact on the shift from peak travel to low-peak travel for car commuters. Zhang et al. [15] investigated the influence of incentive measures on the travel behaviors of office workers on the basis of a questionnaire survey on the Beijing subway system, and found that services related to fast food restaurants, reduced fares, and flexible working hours had a positive effect on avoiding morning rush hour. ...
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Tourists are confronted with congestion caused by concentrated travel during public holidays. In order to guide tourists to make voluntary changes regarding their travel times during holidays, this paper focuses on exploring holiday rush-hour avoidance travel behavior (HRATB) considering psychological factors. First, based on the theory of planned behavior, the effects of psychological factors including attitude, subjective norm, and perceived behavior control on holiday avoidance travel intention and behavior were quantitatively analyzed by the structural equation model. Second, according to those three subjective psychological factors and the three objective factors of age, monthly income, and tourist group, the segmentation method of the latent class model was adopted to explore tourists' preferences with regard to HRATB. Finally, an empirical analysis was carried out through questionnaire data. The results show that attitude, subjective norm, and perceived behavior control have significant impacts on intention and behavior with regard to holiday avoidance travel. There are significant differences in psychological observation variables such as rush-hour avoidance travel intention, attitude and subjective norm among the four segments of tourists, and cost sensitivity. In addition, this paper puts forward some countermeasures and suggestions for the four types of tourists. Conclusions provide a theoretical basis for formulating travel measures to attract different types of tourists.
... Beyond the demand pattern impacted by pricing on networks is the disaggregated travelers' behavior. Ben-Elia et al. applied rewards to affect travelers' behavior on rush-hour avoidance, which implies the feasibility of active demand management (ADM) (26). Besides the fiscal impact, the social incentivization (non-monetary rewarding) is also discussed, e.g., Hu et al. designed a platform where travelers participate in traffic balancing while gaining credits to exchange for discounts or lotteries (27). ...
Article
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One-way carsharing has been regarded as one of the innovative urban transportation modes since the last decade. A fleet imbalance problem frequently occurs in the system, which requires efficient vehicle relocation to cope with. An incentive-based approach could influence the users’ demands and could relieve the pressure on operator-based relocation. This paper presents an incentive-based approach involving a vehicle rewarding policy and a station rewarding policy to attract pick-up demands and drop-off demands respectively. A ranking method is proposed to determine the list of candidate rewarding vehicles and stations. The method acquires the user-app log data and the transaction data in a real operating environment for pick-up and drop-off demand prediction. Five factors for vehicles and four factors for stations are computed from the real-time data. The ranking indices are aggregated from the weighted sum of the factors. The rewarding policy and the ranking method were tested in the real operating environment of an electric vehicle sharing system in two districts of Shanghai. The result suggests that the rewarding policy with ranking method could shorten the vehicle idle time and increase the number of transactions per vehicle and per station, and also resulted in increments on profits.
... For example, a car can be a moving office and provide flexibility to mobile professionals [20,21]. In addition, people can potentially travel more efficiently or change travel modes by accessing real-time travel information [22,23]. Hong and Thakuriah [7] indicated that a substantial number of workers in their sample obtain travel information through apps, websites and systems when they make car trips. ...
Article
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Due to advances in technology (in particular the Internet), people have become less restricted by space and time, and can use travel time more productively by using their Internet-connected mobile devices on the move. Some operators provided Internet access on public transport to increase ridership. This has been shown to increase ridership, however it is not clear if it can induce people who prefer private cars to public transport to consider using public transport. In this paper, we examine the relationship between the frequency of using the Internet while commuting or travelling, and commuting mode choice, and how this relationship varies for people who have different attitudes toward public transport. Our results show that commuters who use the Internet frequently on the move tend to use public transport more. In addition, this association is significant for those who prefer private cars to public transport, showing the potential effectiveness of new technology in generating new riders.
... While a number of studies have examined personal factors that constitute the socio-demographic characteristics of individuals, such as age, gender, education level, profession, income, attitudes, beliefs and lifestyle as other determinants of mode choice [30][31][32][33][34]. Carse, et al. [35] noted that car availability and lower levels of education were associated with car use in daily travel. Ben-Elia and Ettema [36] found that the travelers' choices regarding how to change behavior was influenced by factors including education, habitual behavior, attitudes, and travel information availability. Accordingly, it is thus necessary to investigate the factors which influence travel behavior and reduce CO 2 emissions by considering physical aspects of the built environment and social aspects of individual characteristics. ...
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Shifting toward sustainable daily travel will play a significant role in the future of sustainable development and the lowering of carbon emissions. This study provides an in-depth comparison of transport mode choice and corresponding CO2 emissions between private cars and public transport used for shopping trips based on individual data from a travel survey conducted in Shenyang, China. The analysis found that bus travel accounted for the majority of motorized transportation. Public transport users were closely distributed along the bus or metro lines, and aggregated private car users were mainly clustered within the second circumferential road. Furthermore, average per trip emissions for private car travel were 8-fold that of public transport. Binary logistic regression modeling was employed to examine factors that were related to the choice between private car and public transport, and the results indicated that car ownership and gender were the most important factors in explaining the preference of car driving. Age and per capita monthly income were negatively correlated with car driving. In addition, there were also negative impacts associated to the built environment factors of access to the closest metro stations and the number of bus stops near the residence on car driving. This study is vital to formulate more effective transportation policy measures in the future development for a sustainable low-carbon city. DOWNLOAD HISTORY | This article has been downloaded 775 times in Digital Commons before migrating into this platform.
... The model of [32] had a pyramid structure to incentivize commuters to travel at less congested hours and it was applied in Bangalore. The studies of [33,34] analyzed the effects of incentives (money or credits to keep a smartphone handset) that could be achieved by commuters by avoiding peak hours or switching to another mode, or by smart working; the model has been applied in Netherlands and commuters received from 3 € to 7 € for not driving during peak hours. ...
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Logistics activities, e.g., transportations of goods and people, are responsible for at least one-third of energy consumption and Green House Gas (GHG) emissions. About 70% of them are related to people’s mobility, with millions of cars moving every day. The people home-work logistics represents undoubtedly an important part of it since flows are concentrated on fixed time windows (beginning, lunch break, and end of the working day) creating huge traffic congestions and negative impacts on time, economics, and the environment. This study proposes an integrated model, summarized through a methodological framework, where three actors (companies, public administrations and local shops) work together aiming to economically incentivize the use of sustainable mobility systems. Three are the main elements of the proposed sustainable people home-work logistics model: (1) the economic self-sustainability of the incentives, funded in different ways by the actors, (2) the scalability, thanks to the possibility to add new territories to the project and (3) the territorial circular economy generated thanks to the incentive’s destinations and the public-private integration. Starting from survey questionnaires and territorial attributes, sustainable mobility ways are defined. Then, participant workers are monitored by activating a mobile app, called Ecoattivi, during their home-work journeys. In such a way, workers can directly analyze their sustainable mobility and reach the possibility to accumulate and spend money in local shops as a function of the saved CO2. On the other hand, companies and public administrations compete in a special ranking for sustainable mobility. The methodological framework has been applied to a real case study in the Chiampo Valley, in the northeast of Italy, where about 10 small towns and dozens of companies in 2020 started the “Bike to Work Valchiampo” project.
... Research in travel behavioral psychology indicates that individuals are motivated and act more favorably towards the desired behavior when rewarded, while policies that "punish" the individuals, such as congestion charging, may be ineffective in supporting a lasting change in travel behavior [4,5]. In this context, some papers examine rewards which are offered to travelers to encourage them to avoid rush-hour travelling by cars [5,6,7,8] or public transport [9,10], while a growing body of research focuses on reward schemes that encourage modal shift and the use public transport or non-motorized modes [11,12,13,14,15]. This paper examines different reward schemes, which aim to incentivize users towards green mobility options such as public transport, sharing schemes, active transport or a combination of the above. ...
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This paper aims to investigate the effectiveness of reward-based schemes on altering traveler’s decision making towards sustainable multimodal transportation. For this purpose, a questionnaire survey is conducted in the context of the EC-H2020 funded project “OPTIMUM” within which suitable stated preference experiments are designed. Apart from the traditional multimodal attributes, such as travel time and travel cost, each stated preference experiment is supplemented by an attribute which represents a reward-based scheme. A mixed logit model is estimated where the individual’s utility is linearly dependent on the respondent’s socio-demographics and the attributes of the different multimodal alternatives. Our analysis indicates that, overall, the reward-based incentives could slightly contribute to the promotion of sustainable and emerging transport services. In specific, offering credits and monetary rewards may be effective in altering travellers’ behavior, while the provision of other non-financial passenger services does not influence individuals’ travel choice. In addition, it is found that individuals are more likely to use car-sharing in the presence of monetary rewards, while the alternatives “Public transport with bike-sharing” and “Public transport with Bicycle” are positively affected in the presence of credits.
... Previous studies have analyzed factors influencing commuters' mode choice and reported that in addition to spatial configuration of land use and transport infrastructure at the origin and/or destination, commuters' mode choice -driving alone, carpooling, transit, walking, or biking -was also associated with transportation-related financial incentives, such as free or subsidized transit pass or parking provided by employers (Fujii and Kitamura, 2003;Hess, 2001;Lachapelle, 2017;Thogersen, 2009;Willson and Shoup, 1990;Wilson, 1992;Yang et al., 2015;Zhang et al., 2014). Also, studies have indicated that transportation-related financial incentives reduced the use of single occupancy vehicles during peak-hours (Ben-Elia and Ettema, 2011aEttema, , 2011bEttema et al., 2010;Rey et al., 2016). In this study, using data from the 2011 Atlanta Regional Household Travel survey, we analyze if there was a relationship between free or subsidized transit pass or parking provided by local employers and commuters' decision to use transit in metro Atlanta. ...
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Local employers can play an important role in the transportation or travel demand management (TDM) effort by influencing commuters' mode choice through financial incentives. Using the 2011 Atlanta Regional Household Travel Survey data, this paper analyzes the relationship between free or subsidized transit pass or parking provided by employers and commuters' decision to use transit in metro Atlanta. We find that employees who were provided free or subsidized transit pass had 156% higher odds to commute on transit, but employees who were provided free or subsidized parking had 71% lower odds to commute on transit, all else equal, compared to their counterparts. Hence, encouraging local employers to offer free or subsidized transit pass instead of free or subsidized parking to their employees would be an effective strategy to manage transportation or travel demand in metro Atlanta.
... One possible method for tackling traffic congestion is to make major transformative changes to traditional working practices and the need for commuting (Ben-Elia and Ettema, 2011;Lyonette et al., 2013;Waters, 2007). Until recently, the tools of our labor dictated where work activities needed to be performed, but in the modern digital era many of these tools are now portable or cloud-based, meaning many work tasks can be conducted from any location (Hopkins and McKay, 2014). ...
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As urbanization increases traffic congestion, major transformative changes must be explored to ensure that cities like Ho Chi Minh City (HCMC) transition into equitable, sustainable, and livable cities. This study aims to investigate anywhere working, the practice of performing work tasks remotely instead of from a traditional “fixed” office location, as a possible mechanism for reducing traffic congestion and pollution. The research adopts a descriptive survey method to collect primary empirical data on the current working arrangements and commuting habits of HCMC workers, to identify organizational policies relating to anywhere working, commuter transport modes/usage/timings, attitudes toward anywhere working, and the benefits, constraints/concerns, and perceived productivity for working in locations outside of a traditional office space. The results indicated that, while 74% of HCMC commuters would like to engage in anywhere working practices, only 41% were permitted to do so. This low adoption rate was not necessarily due to the nature of the work tasks themselves, but due to managerial decisions of their employers, and the desire to engage in anywhere working was found to be strongest among those who have already had the first-hand experience of working remotely. HCMC is predicted to be the second fast-growing economy in Asia by 2021 (Tu, 2017) and the findings from this research provide timely and valuable new insights into this phenomenon, as a potential mechanism for assisting the cities of the future develop more equitable, sustainable, and livable conditions through the use of modern Information and Communication Technologies (ICTs).
... Individuals have asymmetric preferences for gains and losses (Kahneman and Tversky, 1979) and people are not comfortable with bearing even small risks. In this context, several studies have confirmed the importance of the availability of travel information to reduce uncertainty and influence travel mode decisions (Srinivasan and Mahmassani, 2003;Ben-Elia and Ettema, 2011a). Changing one's commute causes uncertainty in travel time, travel cost, and commuters may not be sure of whether or not peak-avoidance can relieve congestion effectively. ...
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Travel demand management (TDM) is used for managing congestion in urban areas. While TDM is well studied for car traffic, its application in transit is still emerging. Well-structured transit TDM approaches can help agencies better manage the available system capacity when the opportunity and investment to expand are limited. However, transit systems are complex and the design of a TDM scheme, deciding when, where, and how much discount or surcharge is implemented, is not trivial. The paper proposes a general framework for the optimal design of promotion based TDM strategies in urban rail systems. The framework consists of two major components: network performance and optimization. The network performance model updates the origin-destination (OD) demand based on the response to the promotion strategy, assigns it to the network, and estimates performance metrics. The optimization model allocates resources to maximize promotion performance in a cost effective way by better targeting users whose behavioral response to the promotion improves system performance. The optimal design of promotion strategies is facilitated by the availability of smart card (automated fare collection, AFC) data. The proposed approach is demonstrated with data from a busy urban rail system. The results illustrate the value of the method, compare the effectiveness of different strategies, and highlight the limits of the effectiveness of such strategies.
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In an urban environment, people's daily traffic choices are reflected in emissions and the resulting local air composition, or air quality. Traffic contributes to the emissions of both carbon dioxide (CO2), affecting climate, and particulate matter (PM), affecting atmospheric chemistry and human health. While the development of city infrastructure is not in the hands of individuals, it is their transport mode choices that constitute traffic. In this scoping review we analyse 108 initiatives from around the world potentially influencing individual travel behaviour and producing changes in the shares of different transport modes (modal shifts). The targets, types and techniques of initiatives are identified. Examples of economic, regulative, structural and persuasive initiatives are included. Special focus is on whether the impacts on CO2 emissions, PM emissions and/or PM concentrations have been quantitatively evaluated, and on the quality and results of the evaluations. We observe that a variety of targets can motivate actions that lead to modal shifts and emission reductions. The results indicate that the level of atmospheric evaluations is low: absolute or relative changes in emissions and/or concentrations had been evaluated for only 31% (N = 34) of the reviewed initiatives, with substantial heterogeneity in quality. Sanctions, such as congestion charge and restrictions, have more likely been evaluated in peer reviewed analyses than incentives. Scientific evaluations of impacts on ambient PM concentrations are especially scarce (N = 4), although Air Quality is the primary target of 13% of actions and secondary target for at least 12%. We discuss the determinants of success and failure, when it comes to different types of initiatives, emission reductions and evaluations. A high-quality evaluation of atmospheric impacts captures the following: correct data about the modal shift (rate and direction), exclusion of external factors affecting the shift and emissions, and possible indirect impacts of the shift.
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This study presents results from an investigation into the effect of positive incentives on cycling behaviour among 1802 commuters in the Twente region of the Netherlands. The authors used an on-line survey, which included mock-up apps with incentives to commute to work by bicycle. They tested five reward schemes, namely social rewards (such as badges), in-kind gifts, money, competition, and cooperation. They used the survey data in a multinomial logit model to estimate to what extent travellers will use the app and increase their cycling frequency and which incentives they prefer. The model results show that respondents who sometimes cycle to work are more positive about incentive schemes than respondents who never cycle and that offering an app with in-kind gifts is probably most effective. Interestingly, non-cyclists are more likely to change their behaviour for a reward if they care about travel costs, while occasional cyclists are more likely to cycle more often in response to incentives if they care about attributes that are related to the cycling itself. This also depends on attitudes towards cycling and on socio-demographic variables.
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To ease traffic congestion on the Tohoku expressway during the nationwide summer holiday, we conducted two sets of interventional experiments applying the emotional persuasive strategy to persuade potential Tohoku expressway users to switch to the Joban expressway over a four-week period. Specifically, we first conducted a longitudinal online survey with interventional content to examine the change of intention and behavior on route decisions. At the same time, we provided the same interventional content to another set of users by means of a smartphone application and tracked their location information during the experiment period (12 days within the four weeks) to validate the results of the survey study. The results indicate that: (1) Content with emotional priming significantly increases the detour intention, and has the potential to increase detour behavior. (2) The effects vary depending on additional factors, such as previous travel experience, and the presence of small children. Overall, the study shows that the emotional persuasive strategy is an effective way to change detour intention and behavior.
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We propose an incentive-based traffic demand management policy to alleviate traffic congestion on a road stretch that creates a bottleneck for the commuters. The incentive targets electric vehicles owners by proposing a discount on the energy price they use to charge their vehicles if they are flexible in their departure time. We show that, with a sufficient monetary budget, it is possible to completely eliminate the traffic congestion and we compute the optimal discount. We analyse also the case of limited budget, when the congestion cannot be completely eliminated. We compute analytically the policy minimising the congestion and estimate the level of inefficiency for different budgets. We corroborate our theoretical findings with numerical simulations that allow us to highlight the power of the proposed method in providing practical advice for the design of policies.
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Issues such as the increasing demand for intra-city trips in megalopolises, noticeable growth in the use of public transportation, problems caused during peak traffic hours, and limitations related to the augmentation of transport fleet sizes all underscore the necessity of transportation demand management. In this study, we sought to address the question of whether incentive-based plans can alter passengers’ behavior during peak hours. To accomplish the task, we used 432 Tehran subway system passengers’ stated preferences. The results indicate that incentives, such as ticket discounting during off-peak hours, increasing availability of seats, and offering free Wi-Fi and breakfast discount coupons, can be influential in altering passenger behavior during peak hours. In addition, wait time reductions during off-peak hours can be an incentive for changing the travel behavior.
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Energy subsidies worldwide run into the trillions of dollars, with major negative economic and environmental consequences, but they have proven politically difficult to remove. Here, we study the relationship between fuel prices and the global obesity epidemic using data spanning 145 countries between 1998 and 2016. Low or subsidized fuel makes car travel more affordable, thereby making it more attractive compared to healthier modes of transportation such as walking or cycling. Previous studies provide suggestive evidence, but the link between fuel prices and obesity has yet to be shown over a time-period of decades, or on a global scale. In our models, we consistently find a strong, statistically significant, and negative association between gasoline price and body mass index (BMI). While BMI rose over time almost everywhere, the rate of increase was considerably lower in countries with high fuel prices. Our findings suggest public health advocates have reason to consider joining the coalition of environmentalists and economists already urging reforms to fuel subsidies and taxation.
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This paper proposes a data-driven transport modeling framework to assess the impact of freight departure time shift policies. We develop and apply the framework around the case of the port of Rotterdam. Container transport demand data and traffic data from the surrounding network are used as inputs. The model is based on a graph convolutional deep neural network that predicts traffic volume, speed, and vehicle loss hours in the system with high accuracy. The model allows us to quantify the benefits of different degrees of adjustment of truck departure times towards the off-peak hours. In our case, travel time reductions over the network are possible up to 10%. Freight demand management can build on the model to design departure time advisory schemes or incentive schemes for peak avoidance by freight traffic. These measures may improve the reliability of road freight operations as well as overall traffic conditions on the network.
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Transport data is crucial for transport planning and operations. Collecting high-quality data has long been challenging due to the difficulty of achieving adequate spatiotemporal coverage within a representative sample. The increasingly integrated use of Information and Communication technologies in transport systems offers an opportunity to collect data using non-traditional methods. Crowdsourcing applications are an example where a community of users shares information about their travel experience. However, crowdsourcing applications depend on a critical mass of users providing feedback. We conducted a large-scale field experiment to examine the effect of economic incentives (a lottery for free trips) and cooperation messages (asking users to help the community) to encourage users to share reports about bus stop conditions using a crowdsourcing app. We found that offering an economic incentive increased the participation rate almost three times compared to a control group, which did not receive any message. This positive effect lasted for several weeks but decreased over time, especially for users who had not made reports prior to the experiment. This incentive also increased the number of reports shared by users. Using a cooperation message, with or without the economic incentive, also increased the participation rate compared to the control group, but adding a cooperation message decreased the effect of a standalone economic incentive.
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With rapid population growth and urban development, traffic congestion has become an inescapable issue, especially in large cities. Many congestion reduction strategies have been proposed in the past, ranging from roadway extension to transportation demand management. In particular, congestion pricing schemes have been used as negative reinforcements for traffic control. In this project, we study an alternative approach of offering positive incentives to drivers to take different routes. More specifically, we propose an algorithm to reduce traffic congestion and improve routing efficiency via offering personalized incentives to drivers. We exploit the wide-accessibility of smart devices to communicate with drivers and develop an incentive offering mechanism using individuals' preferences and aggregate traffic information. The incentives are offered after solving a large-scale optimization problem in order to minimize the total travel time (or minimize any cost function of the network such as total Carbon emission). Since this massive size optimization problem needs to be solved continually in the network, we developed a distributed computational approach. The proposed distributed algorithm is guaranteed to converge under a mild set of assumptions that are verified with real data. We evaluated the performance of our algorithm using traffic data from the Los Angeles area. Our experiments show congestion reduction of up to 11% in arterial roads and highways.
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A travel behaviour change approach complements hard transport measures to develop more sustainable transport systems. Travel behaviour change is a growing field of research, with a range of theories, behaviours and tools being studied. Consequently, a wide-angled review is critical for synthesising knowledge in this field. This study conducted a scientometric review of travel behaviour change literature, identifying the main characteristics, key journals, research categories, keywords, authors, institutions, countries and cited references. In addition, a content analysis was conducted to identify current research trends and gaps in the field and develop a future research agenda. The scientometric component of the review analysed the bibliographic data of 323 academic records. The review identified that the field has a long history and has grown significantly since 2011. The content analysis of recent research (n = 17 articles) supported previous findings that travel behaviour change interventions can result in changing behaviour. The main target behaviours are private motor vehicle use, bicycling and public transport. Notably absent is trip avoidance research. Information dissemination strategies are the main tools trialled, including personalised travel plans, websites and apps. Finally, we propose six research directions for the travel behaviour change field: multiple research methods; identify effective intervention components; locally contextualised research; further segmentation research; longer-term studies; and trip avoidance research. Trip avoidance research is most urgent, as experiences due to COVID-19 have shown, working from home could have a significant positive impact on the sustainability of our transport systems.
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When imposing traffic congestion pricing around downtown commercial centers, there is a concern that commercial activities will have to consider relocating due to reduced demand, at a cost to merchants. Concerns like these were important in the debates before the introductions of congestion charges in both London and Stockholm and influenced the final policy design choices. This study introduces a sequential experimental game to study reactions to congestion pricing in the commercial sector. In the game, merchants first make location choices. Consumers, who drive to do their shopping, subsequently choose where to shop. Initial responses to the introduction of congestion pricing and equilibrium selection adjustments over time are observed. These observations are compared to responses and adjustments in a condition where congestion pricing is reduced from an initially high level. Payoffs are non-linear and non-transparent, making it less than obvious that the efficient equilibrium will be selected, and introducing possibilities that participants need to discover their preferences and anchor on past experiences. We find that initial responses reflect standard inverse price–demand relations, and that adjustments over time rely on signaling by consumers leading to the efficient equilibrium. There is also evidence that priming from initial experiences influence play somewhat. We confirm that commercial activities relocate following the introduction of congestion pricing and that the adjustment process is costly to merchants.
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Although congestion pricing has been considered as a key tool of transport demand management (TDM), it is rarely implemented, mainly due to its low public acceptance and resulting political costs. Recently a new approach was suggested: reward desirable behavior rather than punish undesirable behavior. Specifically, positive financial incentives have been suggested to encourage road users to change their departure time, mode of transportation, or route to minimize congestion. This paper makes three contributions to the literature on congestion pricing. First, we offer a comprehensive conceptual examination, reflecting discussions among practitioners in Israel, regarding the positive incentives approach, including various aspects that are related to both positive incentives and congestion tolls, highlighting the differences between the two policies. Second, we use a governmentally-managed pilot with positive incentives that was recently implemented in Israel and which reported important behavioral responses to positive incentives. Third, we use the Israeli experience to examine media discourse regarding congestion pricing policies in general, as well as positive incentive initiatives. We find that the positive incentives pilot demonstrated promising behavioral responses. Moreover, analysis of newspaper articles shows that while the main view of positive incentives is positive, mainly because participation is voluntary, the main attitude toward congestion tolls is negative due to concerns about equity.
Preprint
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When imposing traffic congestion pricing around downtown commercial centers, there is a concern that commercial activities will have to consider relocating due to reduced demand, at a cost to merchants. Concerns like these were important in the debates before the introductions of congestion charges in both London and Stockholm and influenced the final policy design choices. This study introduces a sequential experimental game to study reactions to congestion pricing in the commercial sector. In the game merchants first make location choices. Consumers, who drive to do their shopping, subsequently choose where to shop. Initial responses to the introduction of congestion pricing and equilibrium selection adjustments over time are observed. These observations are compared to responses and adjustments in a condition where congestion pricing is reduced from an initially high level. Payoffs are non-linear and non-transparent, making it less than obvious that the efficient equilibrium will be selected, and introducing possibilities that participants need to discover their preferences and anchor on past experiences. We find that initial responses reflect standard inverse price-demand relation, and that adjustments over time rely on signaling by consumers leading to the efficient equilibrium. There is also evidence that priming from initial experiences influence play somewhat. We confirm that commercial activities relocate following the introduction of congestion pricing and that the adjustment process is costly to merchants.
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Incentives can influence travelers' mobility decisions and thus be used to indirectly redistribute traffic demands in space and in time to reduce congestion. This study aims to understand travelers' mobility decisions in response to customer incentives. An online questionnaire survey was conducted to obtain travelers' opinions and preferences on customer incentives. The results suggest that incentive types and travelers’ personal and social-economic attributes are coupled to affect their acceptance and rejection decisions. This study sheds light on using incentives to switch near-by travelers to potential business customers during peak hours, which ultimately contributes to congestion relief and economic prosperity.
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This article introduces a telematics-based traffic law enforcement and management system (SLEM), which leverages connected vehicle and telematics technologies. The system assigns each driver a real-time score that measures her/his driving performance. Using these driver scores, SLEM then adopts a personalized route guidance strategy that favors high-performing drivers by guiding them to less congested routes at the expense of low-performing drivers who are directed to alternative, slower routes. This routing strategy shifts the network traffic distribution pattern from the undesirable user equilibrium pattern to the system optimal pattern. Hence, SLEM not only incentivizes drivers to improve their driving performance but it also provides a mechanism to manage network congestion. A bilevel mathematical program and an efficient solution methodology were developed to derive SLEM’s optimal routing strategy. A set of experiments that was conducted to evaluate the performance of SLEM under different operation scenarios showed that the adoption of SLEM’s routing strategy reduced travel time during recurrent congestion situations by about 5%.
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With growing concerns about travel demand management practices in overcrowded metro systems, it is considered that time-dependent pricing strategies are effective for dealing with the crowding occurring during peak commuting hours. In this study, a bi-level optimisation framework is used to consider the peak avoidance behaviour of commuters in the development of time-dependent pricing strategies. The behavioural sensitivity of commuters to pricing factors is investigated in terms of departure time and mode shift decisions based on a stated preference survey conducted in Beijing, China. The proposed bi-level programming model comprises a multi-objective optimisation model at the upper level and a nested logit-based stochastic user equilibrium model at the lower level. Based on an empirical case study of the Batong line in Beijing metro, nine optimal time-dependent pricing strategies are tailored by representative decision preferences, yielding an optimal off-peak discount of approximately 30% and best extra peak charge of 135%. Accordingly, the peak ridership is reduced by up to 13.97% during rush hours.
Chapter
Urban transport policies are currently determined by sustainable mobility. The selection and application of appropriate tools, which takes into account transport behaviour and preferences of residents, as well as the analysis of their willingness to change those, are necessary in order to achieve the goals of sustainable mobility. The identification of these determinants and trends requires research to be carried out among the entire cross-section of residents, as well as on particular groups of consumers targeted by the services offered. Working people constitute one of the basic segments of consumers, regardless of the way in which urban trips are completed. The article presents an analysis of changes in transport preferences and behaviour of the aforementioned group of employed consumers resident in Gdynia. It compares results of comprehensive research into transport behaviour and preferences carried out in 2008 and 2015 and shows to what extent over a 7-year period those preferences and behaviour of people in employment changed compared to the entire cross-section of residents between 15 and 75 years of age. During this period an increase in the number of cars per household (from 64% to 72%) as well as an increase in their share in urban trips (from 47% to 58%) was noted. It also evaluates the effectiveness of measures applied in order to achieve more sustainable transport.
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Transport users frequently face the dilemma of maximizing the utility of usage of two resources—time and money in relation to the transport services. The perceived value of the transport services for passengers is the result of what they have received in relation to the costs of transport service. It expresses the concept of generalized cost, which includes financial costs, time costs, discomfort, and other elements that can be perceived in general by the user as costs. The chapter presents the generalized cost on the example of selection of private car and public transport. Car users, while comparing the ability to move by car or by public transport, often do not take into account the full cost of car ownership and usage. While in urban transport homogeneous rates for different tariffs are adopted, whereas in the case of individual transport costs are not limited to the fuel price. The concept of generalized cost is presented in the chapter, based on market research carried out in Szczecin and the proposed method of calculating these costs in transport.
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Incentive-Based Traffic Demand Management (IBTDM) provides monetary incentives to encourage commuters to alter their departures spatially or temporary with the goal of alleviating congestion. With the proliferation of smartphone technology, mobility apps have become ideal platforms for carrying out IBTDM. Tremendous amounts of empirical app usage data have been collected, but research into the behavioral insights of IBTDM remains limited. It is unclear who IBTDM’s target users should be, and which users are the most likely to be stable (actively use the app) and behaviorally sustainable (willing to contribute to congestion alleviation). This study aims to profile the socio-demographics of such favored users based on behavioral and socio-demographic data collected by the Metropia app. The Ensemble Empirical Mode Decomposition (EEMD) method was used for usage trend detection. The detected usage trends were then used in pattern classification to identify stable and sustainable users. Next, binary logistic regression was adopted to explore the socio-demographic characteristics of each category of users. It was found that factors including home work days, household annual income, household size and schedule flexibility played important roles in users’ usage patterns and departure time decisions. Specifically, home work days and household annual income co-influenced app usage patterns. Household size and schedule flexibility were the main determinants of departure time behavior. The findings of this research can be used to guide administrators of budget-constrained IBTDM programs who need to wisely allocate their marketing budget to increase penetration among favored users as to maximize the utility of the program.
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In recent years, there has been a rapid growth of smart apps that could interact with users and implement personalized rewards to coordinate and change user behavior. Understanding user behavior is an enabling factor for the success of these promising apps. However, existing statistical models for modeling user behavior encounter limitations. Choice models based on Random Utility Maximization (RUM) commonly assume that the data collection is independent with the human behavior. However, when users interact with the apps, the real potential and also the real challenge for modeling user behavior is that the apps not merely are data collection tools, but also change users’ behaviors. In this work, we model the user behavior as a graphical model, examine our hypothesis that existing choice models are not suitable, and develop an interesting computational strategy using max-margin formulation to overcome the learning challenge of the our proposed graphical model that is named the Latent Decision Threshold (LDT) model.
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This paper quantifies the causal impact of differential pricing on the trip-scheduling of regular commuters using the Mass Transit Railway (MTR) in Hong Kong. It does so by applying a difference-indifference (DID) method to large scale smart card data before and after the introduction of the Early Bird Discount (EBD) pricing intervention. We find statistically significant but small effects of the EBD in the form of earlier departure times. Leveraging the granularity of the data, we also allow for the treatment effect to vary over observed travel characteristics. Our empirical results suggest that fares and crowding are the key determinants of commuter responsiveness to the EBD policy.
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Research dealing with various aspects of* the theory of planned behavior (Ajzen, 1985, 1987) is reviewed, and some unresolved issues are discussed. In broad terms, the theory is found to be well supported by empirical evidence. Intentions to perform behaviors of different kinds can be predicted with high accuracy from attitudes toward the behavior, subjective norms, and perceived behavioral control; and these intentions, together with perceptions of behavioral control, account for considerable variance in actual behavior. Attitudes, subjective norms, and perceived behavioral control are shown to be related to appropriate sets of salient behavioral, normative, and control beliefs about the behavior, but the exact nature of these relations is still uncertain. Expectancy— value formulations are found to be only partly successful in dealing with these relations. Optimal rescaling of expectancy and value measures is offered as a means of dealing with measurement limitations. Finally, inclusion of past behavior in the prediction equation is shown to provide a means of testing the theory*s sufficiency, another issue that remains unresolved. The limited available evidence concerning this question shows that the theory is predicting behavior quite well in comparison to the ceiling imposed by behavioral reliability.
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Recent policy discussions about information technology in transport and traffic demand management have increased interest in activity‐based approaches to the analysis of travel behaviour, in particular in the modelling of household activity scheduling which is at the core of many of the required changes in travel behaviour. This paper is a state‐of‐the‐art review of conceptualizations and models of activity scheduling with special regard to issues raised by the new policy instruments. In the course of the review, the validity of behavioural assumptions is examined critically and several needs for future research identified.
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Auto restraint policies are becoming increasingly popular among urban planners and policy makers as a way of managing travel demand and traffic in city centers. Because urban access is considered crucial to the economic success of a downtown area, certain constituencies, such as business and retail, have historically been opposed to such policies. To address these concerns and design appropriate policies, it is important to understand how visitors to a city center are likely to respond to new policies. This paper presents a model for estimating the likely response to two potential auto restraint policies in the center of Tel Aviv, the largest metropolitan area in Israel: an increase in parking cost and the use of congestion pricing in the form of a cordon around the city center. The models are based on the responses of center visitors to a stated preference survey. The results show that for both workers and nonworkers, most drivers who respond to the policy will do so by changing their mode of travel, and, in the case of congestion pricing, by also changing the time of their trip. The minority will respond by changing their destination or canceling their trip. This is an encouraging result from a policy point of view because changing time or mode is considered a positive shift, whereas changing destination or canceling the trip is considered negative. The results indicate that auto restraint policies can be effective in reducing traffic congestion and air pollution in city centers without hampering their economic vitality.
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Relying on the theory of planned behavior (Ajzen, 1991), a longitudinal study investigated the effects of an intervention-introduction of a prepaid bus ticket-on increased bus use among college students. In this context, the logic of the proposition that past behavior is the best predictor of later behavior was also examined. The intervention was found to influence attitudes toward bus use, subjective norms, and perceptions of behavioral control and, consistent with the theory, to affect intentions and behavior in the desired direction. Furthermore, the theory afforded accurate prediction of intention and behavior both before and after the intervention. In contrast, a measure of past behavior improved prediction of travel mode prior to the intervention, but lost its predictive utility for behavior following the intervention. In a test of the proposition that the effect of past on later behavior is due to habit formation, an independent measure of habit failed to mediate the effects of past on later behavior. It is concluded that choice of travel mode is largely a reasoned decision; that this decision can be affected by interventions that produce change in attitudes, subjective norms, and perceptions of behavioral control; and that past travel choice contributes to the prediction of later behavior only if circumstances remain relatively stable.
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Conducted 2 laboratory and 1 field experiment with 24, 24, and 8 undergraduates to investigate the effects of external rewards on intrinsic motivation to perform an activity. In each experiment, Ss performed an activity during 3 different periods, and observations relevant to their motivation were made. External rewards were given to the experimental Ss during the 2nd period only, while the control Ss received no rewards. Results indicate that (a) when money was used as an external reward, intrinsic motivation tended to decrease; whereas (b) when verbal reinforcement and positive feedback were used, intrinsic motivation tended to increase. Discrepant findings in the literature are reconciled using a new theoretical framework which employs a cognitive approach and concentrates on the nature of the external reward. (26 ref.) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Travel demand management (TDM) measures targeting changed or reduced private car use in urban areas prompt individuals and households to choose more efficient car use (chaining trips, car pooling, choosing closer destinations), to suppress trips and activities, or to switch travel mode. We conjecture that these choices are made sequentially over time according to a cost-minimization principle. In general, less costly changes may however be less effective. Several potential ways are proposed in which intelligent transportation systems may reduce the costs of changes or reduction in car use, thus presumably rendering TDM measures more effective.
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A model of travel mode choice is tested by means of a survey among 199 inhabitants of a village. Car choice behavior for a particular journey is predicted from the attitude toward choosing the car and the attitude toward choosing an alternative mode (i.e., train), on the one hand, and from general car habit, on the other hand. Unlike traditional measures of habit, a script-based measure was used. General habit was measured by travel mode choices in response to very global descriptions of imaginary journeys. In the model, habit is predicted from the degree of involvement with the decision-making about travel mode choice for the particular journey (decisional involvement) and from the degree of competition in a household with respect to car use. The model proves satisfactory. Moreover, as suggested by Triandis (1977), there is a tradeoff between attitude and habit in the prediction of behavior: When habit is strong the attitude-behavior relation is weak, whereas when habit is weak, the attitude-behavior link is strong.
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Basic principles of applied behavior analysis and social marketing are reviewed with reference to the development of action plans to protect the environment. Behavior-change procedures that have targeted environmental preservation are categorized as antecedent interventions (including education, prompting, modeling, goal setting and commitment, and engineering and design strategies) or consequence procedures (i.e., reinforcement and punishment). Although past behavior analysis research has demonstrated environmental benefits from applying certain behavior-change interventions, those studies were small-scale and short-lived. This paper offers an integrative model of applied behavior analysis and social marketing as a potential approach to large-scale and long-term intervention for environmental protection. The market analysis and segmentation phases of social marketing, for example, allow for the specialization of behavior-change strategies for particular target groups. This integration requires increased collaboration between behavior analysts and environmental psychologists who study the correlation of individuals' environmental concern and action with their attitudinal, demographic, and personality characteristics. A plea is made to replace armchair theorizing with interdisciplinary and intervention-focused environmental research.
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Little children love to play and to learn. They are active, curious, and eager to engage their environments, and when they do they learn. To some extent adults also love to play and to learn. When people are playing and learning in this eager and willing way, they are intrinsically motivated. Throughout life, when they are in their healthiest states, they are active and interested, and the intrinsically motivated behaviors that result help them acquire knowledge about themselves and their world.
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The aim of this study was to investigate whether a temporary structural change would induce a lasting increase in drivers' public transport use. An experiment targeting 43 drivers was carried out, in which a one-month free bus ticket was given to 23 drivers in an experimental group but not to 20 drivers in a control group. Attitudes toward, habits of, and frequency of using automobile and bus were measured immediately before, immediately after, and one month after the one-month long intervention. The results showed that attitudes toward bus were more positive and that the frequency of bus use increased, whereas the habits of using automobile decreased from before the intervention, even one month after the intervention period. Furthermore, the increase in habitual bus use had the largest effect on the increase in the frequency of bus use. The results suggest that a temporary structural change, such as offering auto drivers a temporary free bus ticket, may be an important travel demand management tool for converting automotive travel demand to public-transport travel demand.
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This paper addresses departure time and route switching decisions made by commuters in response to Advanced Traveller Information Systems (ATIS). It is based on the data collected from an experiment using a dynamic interactive travel simulator for laboratory studies of user responses under real-time information. The experiment involves actual commuters who simultaneously interact with each other within a simulated traffic corridor that consists of alternative travel facilities with differing characteristics. These commuters can determine their departure time and route at the origin and their path en-route at various decision nodes along their trip. A multinomial probit model framework is used to capture the serial correlation arising from repeated decisions made by the same respondent. The resulting behavioural model estimates support the notion that commuters' route switching decisions are predicated on the expectation of an improvement in trip time that exceeds a certain threshold (indifference band), which varies systematically with the remaining trip time to the destination, subject to a minimum absolute improvement (about 1 min).
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There is a potential tension between the theoretical desirability of highly differentiated tariff structures and the ability of consumers to respond effectively to them. Evidence from studies of road pricing schemes and tolls, from other transport modes, and from other industries (notably telecommunications), is reviewed and its transferability assessed. Relevant models of human decision making (notably Prospect Theory, Risk Aversion, Ambiguity Avoidance and Bounded Rationality) are explored, and the use and efficiency of heuristics to deal with complex situations is discussed.It is concluded that people have a strong preference for simple tariffs but that they are able to respond to quite complex tariffs provided that they have a clear and logical structure. However, people’s difficulties in estimating distance will severely limit the accuracy of their estimates of distance-based charges and their response to complex pricing signals will be influenced by their attitude to the fairness of the charge. These conclusions are summarised in a general model of response to complex prices.The paper, which reports and extends a study conducted for the UK Department for Transport, concludes by considering the implications for the design and performance of road pricing schemes (an inherent problem being that the theoretically optimum, first-best, pricing structure might be so complicated and dynamically variable that it would be unreasonable to expect road users to predict, let alone respond to, the prices on any given road at any given time – a simpler pricing structure might therefore yield a better overall result).
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Acceptability of travel demand management (TDM) with the aim of reducing private car use is modeled following a hierarchical set of beliefs. In a two-part model, pro-environmental orientation, problem awareness, personal norm, and willingness to reduce car use are linked to beliefs about to which extent the specific TDM measure is perceived to influence freedom to choose travel mode, own reduction of car use, effectiveness, fairness, and subsequently acceptability. Data were collected through a mail survey in Sweden, and the model was tested in a sample of car users for three TDM measures; improved public transport, an information campaign, and increased tax on fuel. First, the models were tested and modified in a randomly selected sub-sample (N=462), then the modified models were validated in the remaining sub-sample (N=460). We conclude that problem awareness and personal norm, in combination with evaluations of specific TDM measures, are underlying the acceptability of TDM measures. Moral considerations and perceived fairness were important for the acceptability of increased tax on fuel, while freedom aspects and problem awareness were of importance for the acceptability of improved public transport. Because acceptability often is important for the implementation of TDM measures, policy makers may draw on these results when attempting to increase the acceptability of various TDM measures.
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This paper focuses on modeling unobserved effects in route-switching dynamics under advanced traveler information systems (ATIS). The analysis explicitly accounts for the presence of heterogeneity in behavior and a general stochastic pattern for the unobservables. The dynamic kernel logit (DKL) framework (also referred to as dynamic mixed logit) is proposed and applied to model route-switching dynamics (with 55 repeated decisions per user), based on data from interactive simulator experiments. In contrast to the multinomial probit framework, the DKL is well-suited for calibrating dynamic travel behavior models with a large number of panel periods. To increase computational efficiency, the proposed formulation exploits a components of variance scheme to represent the correlation of error-terms (both within-day and day-to-day).
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We investigated the hypothesis that a positive attitude towards driving leads to frequent choices of driving which over time make the choices less deliberate or script-based, that is, based on the minimal information required to retrieve scripts stored in memory. In a questionnaire study (study 1) (n=60), the fit of a structural model estimated with maximum-likelihood methods available in LISREL8 confirmed the hypothesized causal relations between rated attitude towards driving, self-reported frequency of driving choice, and degree of script-based driving choice. In Study 2, it was found that participants' (n=48) attitudes towards driving correlated with fictitious driving choice in a laboratory task whereas self-reported frequency of driving choice and degree of script-based driving choice did not. Finally, in Study 3 non-drivers (n=50) repeatedly made fictitious choices between driving or walking and choices of destination. After repeated choices of driving, driving choices were as expected made more frequently when destinations were within walking distance.
Article
Urban Road Pricing has been proposed many times as a powerful instrument to fight congestion in urban traffic, but has systematically faced a hostile political envirionment, due to lack of confidence on its promised (traffic) results and fear of its political consequences. Lack of action in this front is contributing to stable or even growing congestion problems in most large cities.This paper tries to address the problem with a fresh look at the objectives of road pricing and at the reasons for that political hostility. For managing and developing the urban mobility system, efficiency and equity are normally taken as the basic economic objectives. Sustainability objectives may be integrated in the efficiency objective if we are able to represent adequately the costs of the resources consumed in the process. Political hostility is normally based on having to pay for what was freely available, and on the risk of exclusion for those with little revenue available for the extra cost of driving into the city.Pursuit of efficency leads to suggestion of marginal social cost pricing but this is hard to explain to the public and application of this principle is fraught with pitfalls since some components of that cost get smaller as traffic grows (noise related costs for example). Pricing is still a good option but the objective has to be something easier to understand and to serve as a target for mobility managers. That “new” objective is quality of the mobility system, with a meaning similar to that of “level of service” in traffic engineering, and prices should be managed to across space, time and transport modes in such a way that provision of service is made with good quality in all components.Pursuit of equity leads to some form of rationing, which has often been associated with high transaction costs and abuse by the administrators. But the use of electronic road pricing should allow easy ways to address the rationing process without such high costs. The basic proposition is that all local taxpayers receive as a direct restitution of their tax contribution a certain amount of “mobility rights”, which can be used both for private car driving in the tolled areas and for riding public transport.These principles are easily applicable with a variety of technical solutions for road pricing, from the simplest cordon pricing to the more sophisticated “pay-as-you-go” schemes. The paper addresses this question of implementation and argues for increasingly sophisticated schemes, as people get accustomed to the principles and finer targeting of demand segments may be needed.