This study considers how socio-demographic characteristics, mobility situation and attitudes explain current and potential e-bike use in rural areas. Due to longer distances between travel destinations, rural areas in most western societies are characterized by a high car dependence and low use of active modes like walking and cycling. Developing e-bike mobility in these areas can support more healthy and sustainable rural mobility. A large-scale mobility survey conducted among rural residents in the northern parts of the Netherlands provides insight in the determinants of current and potential e-bike use in rural areas. The participant characteristics show that in rural areas also, the e-bike is already used among a broad population of varied ages and backgrounds and for different purposes. Among respondents who did not own an e-bike, especially those with lower socioeconomic status and a household with children showed more willingness to use an e-bike in the future. No evidence was found for current or potential substitution of public transport use. Current e-bike users less likely use a car or regular bicycle as their primary mode of transport. Those who are willing to use an e-bike are less likely to currently use a regular bicycle as their main mode of transport. These findings suggest that the e-bike can substitute both car and bicycle use to some extent. However, bicycle users seem more reluctant towards owning or adopting an e-bike than car users, suggesting greater potential for a shift away from car travel. Furthermore, current and potential e-bike users hold more positive attitudes toward different aspects of e-bike travel than non-users. This provides impetus for future actions to further encourage e-bike use.
Although it is widely reported that rail transit has the potential to encourage higher density development, it remains unclear whether rail transit leads to more mixed urban development across station areas. This article provides rare quantitative analysis of changes in land use mix around the rail transit system in Tianjin, China through an investigation into the spatial effects of a rail transit line which cuts across both highly developed and less developed areas. By using longitudinal data over a twelve-year period (2004–2016) and by comparing the entropy-based land-use mix index, the study shows that with the operation of rail transit, land use mix has increased in formerly low-mixed station catchments, but the change is not obvious for already highly diverse areas. It also shows that a more balanced development occurs in station areas with higher land use dominance, while the leading functions are intensified in station areas with lower land use dominance. By presenting a clear picture of the spatial distribution and patterns of land use mix changes over time, this article concludes that rail transit leads to more balanced development across different station areas in the context of China’s rapid urbanization. The outcome provides a base for further exploring how the planning of rail transit stations may help tackle the differentiated development in cities.
Online food delivery services, provided under the multi-service transport platforms such as Grab and Gojek, could significantly change people’s eating-out behavior, which could also change the spatial distribution of restaurants in the long run. This study attempts to empirically identify factors affecting people’s preference on the use of online food delivery services using stated preference (SP) survey data collected with a multi-day smartphone-based travel diary survey in Jakarta, Indonesia. In the survey, we randomly chose observed eating-out trips (i.e., revealed preference (RP)) from a travel diary and asked whether the respondents would like to shift to an online food delivery service in a hypothetical situation in which the delivery cost, delivery time, food cost, and available food types vary across questions. This RP–SP combination allows us to elicit respondents’ preference under the real time–space constraints they had (e.g., he or she must start to work again from 13:00). Our empirical analysis confirms that delivery time and delivery cost are important factors affecting people’s preference. We also discuss the long-term impact of the behavioral changes on the spatial distribution of online food merchants and its policy implications.
This paper contributes to the public transport literature by ascertaining the role of involvement upon the service quality-satisfaction-behavioral intentions paradigm from the point of view of private vehicle users. This is the first study that provides a comprehensive understanding of this framework based on the private vehicle users’ perspective. The added value of this research is that, by using a structural equation modeling approach, it provides a comparison of alternative models and uses data from different samples collected in five large metropolitan areas (Berlin, Lisbon, London, Madrid and Rome) for modeling validation. In addition, a SEM-MIMIC approach was applied for controlling the heterogeneity of data due to specific characteristics of the interviewee (territorial setting, place of residence, demographic and socio-economic characteristics and travel related variables). The findings show that involvement is a full mediator between satisfaction and behavioral intentions, and that satisfaction is a full mediator between service quality and involvement. Furthermore, the SEM-MIMIC results revealed that the four latent factors investigated (service quality, satisfaction, involvement and behavioral intentions) dealt with highly heterogenous data. However, the most important finding is that private vehicle users’ involvement is the factor that contributes most to their behavioral intentions towards public transport. Hence, public transport managers might benefit from these outcomes when establishing detailed policies and specific guidelines for public transport systems to engage private vehicle users in a higher degree of usage of public transport services.
Ambitious goals to combat pollution should be supported in policies that discourage the use of private cars, notably old and more polluting vehicles. Price signals, such as a congestion tax, and traffic restrictions, such as low-emission zones (LEZ), are widely used tools among European cities to limit car use. In this paper, we look at the dissuasive effect of the implementation of the Madrid Central LEZ and analyze how traffic intensity has been affected in both the restricted area and in other zones of the city. Although the ultimate policy goal of LEZ is to reduce pollution, the instrument considered is traffic limitations, so it is important to know whether or not traffic intensity has been affected by traffic restrictions. Despite its limited extension and the adoption of long transitional periods, the LEZ of Madrid has been seriously questioned from its inception. The results show that traffic intensity has been reduced in the Madrid Central zone but has unfortunately increased in bordering areas. Previous studies on the effects of Madrid Central have not taken into account this potential substitution effect. The future design of a mobility policy in the metropolitan area of Madrid should address this undesirable outcome.
This paper suggests an alternative approach to estimate the value of travel time (VTT) savings, using a case study with exogenous variation in travel costs and data from automatic traffic counts (ATC). With this revealed preferences approach, we address a possible bias of VTT estimates because of self-selection. Compared to the VTT estimates used in transport appraisals, the results produce substantially higher estimates of VTT. Unfortunately, our analysis does allow us to distinguish the self-selection bias from other possible sources of bias. The cost of using ATC data is that there is no direct information regarding the motorists, and the analysis must be done using aggregated data at an hourly interval. Still, this alternative approach may complement the results with more detailed data.
Hundreds of millions of internal migrants are present in cities in the developing world. Accurately predicting their commuting mode choice and clarifying the effect size of the influencing factors have become increasingly indispensable for formulating and executing socially inclusive transportation plans and policies. Yet, scholarship on internal migrants’ travel mode choice is still scarce, particularly in the context of developing country. This study attempts to partly fill this gap. Using empirical data from Xiamen, China, it applies the Light Gradient Boosting (LightGBM) approach, a high-efficiency and high-performance machine learning framework, to predict the commuting mode choice of both the internal migrants and locals. The results show that (1) The built environment has larger impacts on locals’ mode choice than on migrants’; (2) For both the migrants and locals, the relative importance of the built environment in predicting commuting mode choice exceeds that of socio-demographics and trip characteristics; (3) Distance to the closest commercial center is the most important factor influencing commuting mode choice of both groups, and bus stop density also contributes a great deal; hence, regional accessibility and transit infrastructure can be given higher priority in intervening the commuting behaviors of migrants and locals. (4) The LightGBM models yield rather high prediction accuracy; their results are further compared with those of conventional discrete choice models and are found to be generally consistent with the latter. Those findings can help inform decision-makers about nuanced policies concerning meeting locals’ and migrants’ travel demands.
Accurate prediction of short-term passengers’ origin and destination (OD) demands is key to efficient operation and management of urban rail transit (URT), especially in the case of congestion or an incident. However, short-term OD demand forecasting is more challenging than passenger flow forecasting, due to its uncertainty and high dimensions. So far, most OD prediction models capture the spatio-temporal dependencies of OD flow by means of training models on historical data, but what characteristics and laws influence the performance of OD prediction are still unknown. In this paper, we propose temporal Pearson correlation coefficients and approximate entropy, as well as spatial correlations, as indicators to reflect the inherent time–space correlations and complexity of the OD flow. Then, by analyzing automatic fare collection data of the Beijing and Shanghai URT system, this paper deeply discusses the relationships between the spatio-temporal correlations and complexity of the OD flow and the predictive performances of different models with regard to different intervals. Finally, this paper proposes the predictable problem of travel demands and points out that the spatial correlations of the OD matrix are more important than the temporal correlations and complexity in the short-term prediction of travel demands. In particular, the number of principal components of the OD flow can be a key indicator to measure the forecasting performance of a model. A reasonable interval is very important for short-term OD forecasting, and in the Beijing URT system, 30 min is a preferable choice for workdays and 50 min for weekends. All these findings are beneficial to guide users to build a suitable model or improve the existing model to obtain better prediction performances.
Travellers account for variability in transport system performance when they make choices about routes, modes and destinations. Modellers try to quantify travel time reliability through various dispersion measures, most commonly the standard deviation of travel time. However, standard deviation is only one attribute of the nuanced travel time distribution. This paper considers whether standard deviation is sufficient to describe the travellers’ understanding and value of travel time reliability and how we might include other aspects of variability such as the frequency of exceeding a lateness threshold or the likelihood of rare events. Car drivers in New South Wales, Australia, were asked to reconstruct the distribution of their commuting time and identify a lateness threshold. Further, we asked them about their preferences in a series of stated choice experiments using three representations of travel time reliability pivoted around their regular commute. The results show reliability ratios consistent with those in the literature for all three presentations. Moreover, the standard error of the estimated coefficient on the risk of rare events indicates that standard deviation alone may not sufficiently capture travellers’ preferences towards travel time reliability.
This paper focuses on empirically investigating the inertia effects of past behavior in commuting modal shift behavior and contributes to the current state of the art by three aspects. Firstly, this study introduces and tests the potential influences of the inertia effects of past behavior on the traveler’s preferences regarding level-of-service (LOS) variables, besides the impacts of inertia effects on the preference for the frequently used transport mode in the past. Secondly, the mode-specific inertia effects are investigated to distinguish the differences in the inertia effects for different transport modes based on posterior individual-specific parameter estimations. Thirdly, the factors contributing to the heterogeneity of inertia effects including demographics and travel contexts, are quantitatively examined. A joint random parameter logit model using a revealed and stated preference survey regarding commuting behavior is employed to unravel the three aspects. The results reveal significant interactions of inertia terms with LOS variables indicating the influences of past behavior on travelers’ evaluations on attributes of their previous choices. The mean values and variances of inertia effects for different transport modes are significantly and substantially distinct. For instance, the inertia effects of frequently using car are substantially positive representing strong stickiness to the car, while the inertia effects of frequently using the metro have large variances among travelers and mostly appear as dispositions to change. Besides, the effects of personal characteristics and travel contexts on the magnitude of the inertia effects of different transport modes are identified as well. A demand estimation analysis is utilized to investigate the influences of three aspects on predicting travel demands in various contexts. Incorporating the interactions and mode-specific inertia effects can remarkably improve the model performance. The demand estimation will be biased if they are neglected.
The value of freight travel time savings (VFTTS) is a monetary value that is considered an important input into cost–benefit analysis and traffic forecasting. The VFTTS is defined as the marginal rate of substitution between travel time and cost and may therefore differ across firms, time and countries. The paper aims to explain variations in the VFTTS by using the meta-analysis method. The analysis covers 106 monetary valuations extracted from 56 studies conducted from 1988 to 2018 in countries across the globe. The meta-analysis method determines the factors that have an impact on these VFTTS variations. The paper briefly introduces the VFTTS concept and describes the adopted meta-analysis methodology, wherein different meta-models are used in VFTTS estimations. The results highlight the necessity of including multiple explanatory variables to ensure adequate explanation of the VFTTS variations. The findings also show that GDP per capita, transport mode and type of survey respondent are statistically significant variables. The paper sheds some light on the variations, thereby advancing the understanding of each factor’s effects on the VFTTS. Furthermore, meta-model outcomes are used to generate new values of travel time savings for different transport modes in freight transport, for several countries. These implied VFTTS can be used as benchmarks to assess existing evidence or provide new evidence to countries where no such values exist.
Previous studies have indicated that factors such as the built environment, attitudes and past behaviour can influence travel behaviour. However, the possible effect of travel satisfaction on travel mode choice remains underexplored, despite many studies focusing on travel satisfaction over the past years. It is likely that individuals experiencing satisfying trips with a certain travel mode will use this mode (more) frequently for future trips. In this study—using data from 984 students from Laval University, Canada—we analyse how satisfaction with public transport and the frequency of public transport use affect the intention to use public transport in later life stages. Our results indicate that public transport frequency, public transport satisfaction and the interaction between these two factors (i.e., the frequency of (dis)satisfying public transport trips) significantly affect people’s intentions to use public transport in later life, although variations in effect sizes exist between different life stages. Making public transport more pleasant and increasing ridership of children and young adults (e.g., by giving them free public transport passes) may consequently result in a higher public transport frequency in later life stages. We argue that travel satisfaction can play an important role in the formation of habitual mode use, and that satisfying trips (if undertaken frequently) are likely to be repeated in the future.
Network capacity, defined as the largest sum of origin–destination (O–D) flows that can be accommodated by the network based on link performance function and traffic equilibrium assignment, is a critical indicator of network-wide performance assessment in transportation planning and management. The typical modeling rationale of estimating network capacity is to formulate it as a mathematical programming (MP), and there are two main approaches: single-level MP formulation and bi-level programming (BLP) formulation. Although single-level MP is readily solvable, it treats the transportation network as a physical network without considering level of service (LOS). Albeit BLP explicitly models the capacity and link LOS, solving BLP in large-scale networks is challenging due to its non-convexity. Moreover, the inconsideration of trip LOS makes the existing models difficult to differentiate network capacity under various traffic states and to capture the impact of emerging trip-oriented technologies. Therefore, this paper proposes the α -max capacity model to estimate the maximum network capacity under trip or O–D LOS requirement α . The proposed model improves the existing models on three aspects: (a) it considers trip LOS, which can flexibly estimate the network capacity ranging from zero to the physical capacity including reserve, practical and ultimate capacities; (b) trip LOS can intuitively reflect users’ maximum acceptable O–D travel time or planners’ requirement of O–D travel time; and (c) it is a convex and tractable single-level MP. For practical use, we develop a modified gradient projection solution algorithm with soft constraint technique, and provide methods to obtain discrete trip LOS and network capacity under representative traffic states. Numerical examples are presented to demonstrate the features of the proposed model as well as the solution algorithm.
The planning of on-demand services requires the formation of vehicle schedules consisting of service trips and empty trips. This paper presents an algorithm for building vehicle schedules that uses time-dependent demand matrices (= service trips) as input and determines time-dependent empty trip matrices and the number of required vehicles as a result. The presented approach is intended for long-term, strategic transport planning. For this purpose, it provides planners with an estimate of vehicle fleet size and distance travelled by on-demand services. The algorithm can be applied to integer and non-integer demand matrices and is therefore particularly suitable for macroscopic travel demand models. Two case studies illustrate potential applications of the algorithm and feature that on-demand services can be considered in macroscopic travel demand models.
This article examines the differences in commuting length between native and immigrant employees in Spain, a relevant issue since immigrants' longer commuting times may, among other factors, reflect an imperfect spatial matching of their labour supply and demand with negative implications for their relative labour outcomes and their individual well-being. The research differentiates immigrants according to their origin and is based on a rich, nationally representative database. A novel contribution of the research is the use of decomposition econometric techniques that allow quantifying the joint and individual influence of a wide range of explanatory factors. The evidence obtained shows that, although a relevant part of the explanation of the greater commuting observed for immigrants is related to observed elements such as a different use of modes of transport, they make overall significantly longer journeys when comparing with observationally similar natives. This commuting penalty occurs yet only in the case of immigrants from emerging countries as it does not exist for those from advanced economies. Although the penalty is overall rather similar along several sociodemographic and occupational lines, it is much more pronounced for individuals living in large municipalities, which implies that previous analyses focusing on specific densely populated territories could not be nationally representative. To conclude, we offer additional novel evidence about the potential explanations of the commuting penalty of immigrants showing that it does not seem to derive from a hypothetically greater tolerance to commuting.
Parking supply is one of the most neglected elements of the built environment in travel behavior research, despite evidence linking parking with vehicle use. As transportation impacts of new development are increasingly measured by vehicle miles traveled (VMT), explicitly connecting parking characteristics with vehicle travel is necessary to better inform transportation and land use policy. In this paper, we begin to address this research gap and explore the relationship between constrained parking and household VMT. Utilizing the 2017 National Household Travel Survey (NHTS) California add-on sample, we estimate residential parking constraint for households in Los Angeles County. Then, we develop a two-level model framework. Level 1 (Cost) models estimate travel costs, represented by vehicle ownership as a function of parking constraints, the built environment, and demographics. Level 2 (Demand) models regress household-level total and homebased-work VMT on predicted vehicle ownership, controlling for temporal and environmental characteristics. To further explore the relationship between parking and VMT by place type, we applied Level 1 and Level 2 models to develop a suite of scenarios for typical households in Los Angeles County. Our findings support the hypothesis that the built environment (including parking) influences VMT through travel costs (vehicle ownership). Results from scenarios analysis reveal constrained on-site residential parking (< 1 parking space per dwelling unit), accounts for an approximate 10–23 percentage-point decrease in VMT within each place type. Finally, implications for practice and future research are presented.
Carsharing contributes to sustainable urban mobility by reducing private car ownership and use. Thus, policy-makers and planners need to know how cities can foster carsharing and the related benefits. Decentralized mobility hubs are an emerging approach to supporting carsharing. These hubs provide designated carsharing parking spots in the public street spaces of urban residential neighborhoods. The objective is to embed carsharing services into the immediate residential environments of urban households. Thus, the hubs are intended to make carsharing more accessible, reliable, and convenient. However, there is a lack of empirical insights into the impact of decentralized mobility hubs on carsharing. This research uses survey data on carsharing users in the inner city of Hamburg, Germany, to appreciate the actual effects of such hubs on car ownership, transport mode usage, and the perception of carsharing. Decentralized mobility hubs have existed in several high-density residential neighborhoods in Hamburg since 2017. Our findings suggest that the use of these hubs leads to a substantially more positive perception of carsharing and, as a consequence, to a greater willingness of carsharing users to forgo car ownership. Ultimately, by supporting the reduction of private car ownership, the hubs promote not only carsharing, but also the use of other sustainable modes of transportation.
A range of studies have found that immigrants generally start out using different travel modes but over time they ‘assimilate’ toward adopting similar travel modes to the general population. These studies tend to focus on ‘when’ and ‘if’ travel assimilation occurs, with some studies using socioeconomic factors to explain ‘why’ this occurs. But few studies have explored the role of culture, attitudes and other ‘soft’ factors in shaping the process of travel assimilation among immigrants. In Australia, South Asians have been the largest and fastest growing immigrant group, and as skilled migrants they face few ‘hard’ barriers to car use. The aim of this paper is to explore the interaction between cultural influences, attitudes and initial travel experiences upon arrival in Australia on long-term travel assimilation amongst South Asian immigrants. Qualitative interviews with 20 South Asian immigrants were used to identify a range of cultural and psychosocial factors, such as perceptions towards travel modes and gender-based cultural norms. Attitudes and behaviours evolve during their early years in Australia, beginning with a ‘honeymoon period’ – a phase where all travel modes are seen as positive – before car use begins to dominate. The findings have implications for how we understand the interactions between attitudes, cultural practice and travel behaviour and how they evolve over time. They also imply that policymakers have only a narrow window of time to encourage sustainable transport among South Asian immigrants before the travel ‘honeymoon period’ wears off.
This paper studies whether Peru’s excise tax refund of 30% for fuel purchases has had the effect of increasing formalization in the intercity passenger ground transport market and whether the resulting tax expenditure has been fruitful in increasing investment in vehicles. Based on a partial equilibrium market framework, transport operators are segmented among formal, informal and illegal operators, and a new methodology to measure how the tax refund makes these operators change their market shares is perfected. Evidence shows that the tax refund has worked by augmenting the market share of formal but not informal operators, thus increasing the overall formalization of the passenger transport market. This contradicts the Ministry of Finance impact evaluation asserting that market formalization has not occurred because informal operators did not take advantage of the tax refund and did not convert into formal operators. An epilogue challenges a 2020 government decree establishing a new tax refund no longer intended to reduce informality but rather to reduce accident rates.
Immobility – i.e. no travel outside the home in a 24-hour day – is an important issue because it concerns a large part of the population and tends to recur frequently, as our results show. Two questions related to immobility have been particularly highlighted in the literature: firstly, whether immobility is an artefact of travel surveys; secondly, whether it corresponds to an extreme form of low mobility. In light of the literature review and levels of immobility observed, these two questions seem to be minor, particularly in relation to the activity of individuals, which remains the main factor in immobility. By using Structural Equation Modeling to process UK National Travel Survey data, this work has explored the individual variability of trips as a constituent element of immobility for employees and retirees. The link between immobility and variability manifests itself in two ways in our results. Firstly, immobility is associated with activities that are less constrained in time and space, as is the case with the lower frequency of travel for work and support. Secondly, there are rebound effects on mobile days, with more frequent trips for grocery/medical motives in particular, when there is an episode of immobility during the week.
Different from most existing studies in the literature, this study proposes and empirically examines an integrated model to understand user experience and behavior based on actual experiences with a real-world MaaS platform. Specifically, the relationships among perceived service features (i.e. platform and mobility services), perceived value, satisfaction, and behavioral intention of existing MaaS users were examined using a survey sample of 363 existing MaaS users in Taiwan. Results reveal that the most important variable explaining intention to use MaaS is mobility benefits derived from using MaaS, more so than cost/economic benefits, access to greater information, and ease of transaction. MaaS operators need to prioritize service features that offer access to newer modes, more frequent services, covering greater network areas. While other measures such as discounted pricing, provision of dynamic and real-time information, integrated ticketing and payment are valuable, they are unlikely to prove popular with consumers unless the MaaS service can offer a substantial benefit in terms of access to new and expanded transport services. Implications and recommendations for future research are also discussed.
Research shows that immigrants face greater mobility constraints in an unfamiliar environment, particularly immigrant women. But whether and how immigrant women adjust their household-serving trip frequencies accordingly remains largely unknown. Utilizing data of adult male and female members in nuclear families from the 2017 National Household Travel Survey conducted in the United States and applying the negative binomial regression model, it is found that, all else holds constant, being a woman correlates with making more escorting and shopping trips, but being an immigrant does not. In other words, immigrant women escort others and go shopping as often as native-born women. However, it is also found that, all else being equal, spouses of immigrants make more escorting and shopping trips than spouses of natives, especially husbands of immigrant women. Taken together, these results suggest that although men with an immigrant wife take on more household-serving travel duties so that the within-household gender travel gap narrows, the household-serving travel burden on immigrant women is not lessened compared to that on native-born women. Therefore, while encouraging male participation in household labor is a common strategy to reduce women’s domestic load, assistance from outside the home should be provided in particular to immigrant women for alleviating their household-serving travel burden.
In the transport policy development process, four-step models are commonly used to estimate transport costs and flows based on representations of travel demands and networks. However, these models typically do not account for broader changes in the economy, which may significantly shift travel patterns in the case of larger transport projects. LUTI models are often applied to simulate changes in land-use patterns, and regional production function models have been used to estimate changes in production, but these methods rely on fixed economic parameters that may not capture the structural economic changes induced by large transport projects. In a separate line of development, computable general equilibrium (CGE) models, which simulate entire economies, have been increasingly applied to estimate the magnitude and distribution of economic impacts from transport improvements both spatially and through markets, including GDP and welfare. Some CGE models are linked with transport network models, but none incorporate detailed networks or generate a complete set of travel demands. This paper presents an integrated CGE and transport model that generates household and freight trips and simulates a detailed road network for different time periods, such that the transport submodel can be calibrated and run as a conventional transport model. The model provides a tool for the rapid strategic assessment of transport projects and policies when economic responses cannot be assumed to remain static. In the model, the CGE submodel simulates the behaviour of households and firms interacting in markets, where their behaviour takes trip costs into account. The model then generates trips as a derived demand from agent activities and assigns them to the road network according to user equilibrium, before feeding back trip costs to the CGE submodel. The model is then tested by simulating the WestConnex motorway project under construction in Sydney, with results showing significant increases in welfare for regions close to the improvements. Further development of the model is required to incorporate land-use and mode choice.
First-mile last-mile (FMLM) mobility services that connect riders to public transit can lead to improved transit accessibility and network efficiency if such services are convenient and reliable. However, many current FMLM services are inefficient and costly because they are inflexible (e.g., fixed supply of shuttles) and do not leverage collected data for optimized decision making. At the same time, new forms of shared mobility can provide added flexibility and real-time analytics to FMLM systems when carefully integrated. This study evaluates performance and cost implications of public/private coordination between transit shuttles and transportation network companies (TNC) in the FMLM context. A real-time operations model was developed to simulate daily operations for an existing FMLM system using real-world demand data. Three supply strategies were tested with varying levels of flexibility: (1) Status Quo (two 23-passenger on-demand shuttles), (2) Hybrid (one 23-passenger on-demand shuttle + TNC), and (3) TNC Only (exclusively use TNC services). Results indicated that the added flexibility of the Hybrid service design (using shuttles and TNCs) improved service performance (a 7.7% improvement), reduced daily operating costs (− 6.0%), and improved service reliability (95th percentile travel times decreased by up to 40% during peak periods). In addition, the Hybrid service design was more robust to variations in demand. The Hybrid service was significantly cheaper to operate (− 31.6%) at reduced demand levels (50% of normal), and improved service performance (a 10.2% improvement) when demand levels were increased (150% of normal). These findings emphasize the importance of flexibility in FMLM service designs, especially when demand is sparse and variable.
Ridesplitting is a pooled version of a ridesourcing service that can reduce emissions per trip compared with exclusive ridehailing. However, it only takes a small share of the ridesourcing market currently. Fiscal and social incentives are potential leverage tools to attract passengers from solo hailed rides. This study aims to quantitatively identify the effects of carbon credits (CC) and monetary rewards (MR) on people's willingness to adopt ridesplitting. The theory of planned behavior realized by structural equation modeling decomposes the factors which affect passengers' intention and actual action on choosing ridesplitting. Confirmatory factor analysis suggests that pro-environmental attitudes do not directly force people to ridesplitting. Path effect analysis shows that subjective norms and perceived behavioral control significantly affect ridesplitting intentions. Immediate effect regression reveals that CC has greater direct effects on ridesplitting intention than MR (4.5 times more effective). Moderating effect analysis shows the MR effect reduces while the CC effect enhances when increasing the incentive value. The results encourage policymakers and operation designers to consider better marketing schemes combining monetary and social incentives to promote ridesplitting and to gain better fiscal and environmental benefits from ridesourcing systems.
This article presents the MOBIS dataset and underlying survey methods used in its collection. The MOBIS study was a nation-wide randomised controlled trial (RCT) of transport pricing in Switzerland, utilising a combination of postal recruitment, online surveys, and GPS tracking. 21,571 persons completed the first online survey, and 3680 persons completed 8 weeks of GPS tracking. Many continued tracking for over a year after the study was completed. In the field experiment, participants participated through the use of a GPS tracking app, Catch-my-Day, which logged their daily travel on different transport modes and imputed the trip segments and modes. The experiment lasted 8 weeks, bookended by two online surveys. After the first 4-week control phase, participants were split into two different treatment groups and a continued control group. An analysis of the survey participation shows that the technology is capable of supporting such an experiment on both Android and iOS, the two main mobile platforms. Significant differences in the engagement and attrition were observed between iOS and Android participants over the 8-week period. Finally, the attrition rate did not vary between treatment groups. This paper also reports on the wealth of data that are being made available for further research, which includes over 3 million trip stages and activities, labelled with transport mode and purpose respectively.
The online version contains supplementary material available at 10.1007/s11116-022-10299-4.
In this paper, a heuristic method which contributes to the solution of the Daily Activity Chains Optimization problem with the use of Electric Vehicles (DACO-EV) is presented. The DACO-EV is a time-dependent activity-scheduling problem of individual travelers in urban environments. The heuristic method is comprised of a genetic algorithm that considers as its parameters a set of preferences of the travelers regarding their initial activity chains as well as parameters concerning the transportation network and the urban environment. The objective of the algorithm is to calculate the traveler’s optimized activity chains within a single day as they emerge from the improved combinations of the available options for each individual traveler based on their flexibility preferences. Special emphasis is laid on the underlying speed-up techniques of the GA and the mechanisms that account for specific characteristics of EVs, such as consumption according to the EV model and international standards, charging station locations, and the types of charging plugs. From the results of this study, it is proven that the method is suitable for efficiently aiding travelers in the meaningful planning of their daily activity schedules and that the algorithm can serve as a tool for the analysis and derivation of the insights into the transportation network itself.
With the popularization of Internet technologies and shared mobility services, online ridesharing has developed rapidly in numerous cities worldwide. However, perhaps owing to the lack of empirical data, there is a lack of comprehensive and comparative studies on the two major online ridesharing modes, namely, ridesplitting and carpooling, vis-à-vis operational performance discrepancies. Thus, we conduct an empirical study using the massive amount of actual operating data provided by DiDi Chuxing. Based on an analysis of the operating characteristics of ridesplitting and carpooling, this study proposes an approach to estimate ridesharing fuel-saving and distance-saving performance by combining the vehicle operating information and fuel economy indicators of various transportation modes. Furthermore, the operational performance discrepancies between the two major ridesharing modes are compared through an analysis of the user characteristics and interactive effects between ridesharing and subway systems. The results show that the average fuel-saving and distance-saving ratios of ridesplitting are lower than those of carpooling. From the perspective of the transportation system’s fuel economy, ridesharing is not considered to be fuel-saving, and its scale should be reasonably regulated. According to driver classification, carpooling is more suitable for commuting and intercity transportation. In addition, ridesplitting and carpooling can be employed as feeders into subway networks in suburban areas. These findings are believed likely to be beneficial for facilitating the sustainable and standardized development of these two ridesharing modes.
We study the impact of a tightening of a private driving restriction in Germany’s capital, the city of Berlin, on house prices in its affluent suburbs. Using geo-referenced data on train stations, motorway access points and offers of single-family houses for sale from Germany’s leading online property broker ImmobilienScout24 in a spatially staggered DiD framework, we find evidence for sizeable price growth premia for houses located in walking distance of train stations that lie within 30 min commuting duration to Berlin main station. Property located in immediate vicinity (5 min walking distance) of train stations within 30–40 min commuting duration, however, face penalties. Our findings are of relevance for the design of public infrastructure planning policies that seek to accommodate and facilitate changes in local demands for alternative and more environmentally sustainable modes of transport induced by private driving restrictions.
We analyze what ridehail drivers do when searching for paid fares. We use a dataset of 5.3 million trips in San Francisco and partition each search trip into cruising, repositioning, and parking segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find evidence of racial disparities, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color.
Studies on the impact of changes in travel costs on car and public transport use are typically based on cross-sectional travel survey data or time series analysis and do not capture intrapersonal variation in travel patterns, which can result in biased cost elasticities. This paper examines the influence of panel effects and inertia in travel behaviour on travel cost sensitiveness, based on four waves of the Mobility Panel for the Netherlands (comprising around 90,000 trips). This paper analyses the monetary costs of travel. Panel effects reflect (within wave) intrapersonal variations in mode choice, based on three-day trip diary data available for each wave. The impact of intrapersonal variation on cost sensitiveness is shown by comparing mode choice models with panel effects (mixed logit mode choice models with error components) and without panel effects (multinomial logit models). Inertia represents variability in mode choice between waves, measured as the effect of mode choice decisions made in a previous wave on the decisions made in the current wave. Additionally, all mode choice models include socio-economic and spatial variables but also mode preferences and life events. The effect of inertia on travel cost elasticities is measured by estimating mixed logit mode choice models with and without inertia effects. The main conclusion is that the inclusion of intrapersonal effects tends to increase cost sensitiveness whereas the inclusion of inertia effects decreases travel cost sensitiveness for car and public transport modes. Car users are identified as inert travellers, whereas public transport users show a lower tendency to maintain their usual mode choice. This paper reveals the inertia effects over four waves of repeated respondent’s data repeated yearly.
This paper investigates the non-work activity participation and time allocation decisions of couples of low- and high-income households by utilizing primary activity-travel behavior data collected from Bhubaneswar, India. The study estimates a multivariate Probit model for the decision to participate and a multivariate Tobit for the time allocation to out-of-home child maintenance, household maintenance, and recreational activities. The inter-and intra-personal linkages and the nature of the interaction (substitution or complementary effect) associated with the decisions are captured using the models. The analysis reveals that the percentage of high-income female heads participating in recreational activities is approximately 12% more than that of low-income female heads. Regarding time allocation to recreational activities, an average high-income female head spends 28 min more than a low-income female head. In comparison, a low-income mother allocates approximately 13 min more than an average high-income mother to household maintenance activities. The correlation coefficient associated with activity participation and time allocation decisions in low-income families reveals substitution effects regarding the inter- and intra-personal interactions. At the same time, the complementary nature of the interaction was reported for their high-income counterparts. In addition, it is observed that the activity participation decisions are more pronounced between spouses of high-income households compared to low-income families. In contrast, the time-allocations decisions in both household types are relatable. Further, possessing a driving license and owning a car significantly increases high-income female heads’ participation and time allocation in non-work activities. The models also reveal that locating inside the municipality increases female heads’ likelihood of participating in recreational activities. Low-income female heads tend to decrease recreational activity time use if they are within the municipality limits. High-income mothers, on the other hand, are observed to increase durations. An increase in distance to school from home significantly reduces male heads’ involvement in out-of-home child maintenance activities for both household types.
There are significant spatiotemporal correlations among the traffic flows of neighboring road sections in the road network. Correctly identifying such correlations makes an essential contribution for improving the accuracy of traffic flow prediction. Many efforts have been made by several researchers to solve this issue, but they assume that the spatiotemporal correlations among traffic flows are stationary in both time and space, i.e., the degrees to which traffic flows affect each other are fixed. In this study, we propose a clustering based traffic flow prediction method that considers the dynamic nature of spatiotemporal correlations. In order to express the short-term dependence between the target road section and neighboring ones, the spatiotemporal correlation matrices are introduced. The historical traffic data are divided into several clusters according to the similarity between spatiotemporal correlation matrices. The spatiotemporal correlation analysis and the predictor selection based on the mutual information are performed in each cluster, and the multiple prediction models are trained separately. A prediction model corresponding to the cluster to which the current traffic pattern belongs is selected to output the prediction result. Experimental results on real traffic data show that the proposed method achieves good prediction accuracy by distinguishing the heterogeneity of spatiotemporal correlations among the traffic flows.
Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.
Bicycling is an increasingly popular mode of travel in Canadian urban areas, like the Greater Toronto and Hamilton Area (GTHA). While trip origins and destinations can be inferred from travel surveys, data on route choice is often not collected which makes it challenging to capture the attributes of routes travelled by people who cycle. With new algorithms for cycle routing it is now possible to infer routes. Using bicycle trip records from the most recent regional travel survey, a spatial interaction model is developed to investigate the built environment correlates of bicycling flows in Hamilton, Ontario, a mid-sized city part of the GTHA. A feature of the analysis is the use of CycleStreets to compare the distance and time according to different routes inferred between trip zones of origin and destination. In addition, network autocorrelation is accounted for in the estimated models. The most parsimonious model suggests that shortest-path quietest routes that minimize traffic best explain the pattern of bicycle trip flows in Hamilton. Commercial and office locations and points of interest at the zone of origin negatively correlate with the production of trips, while different land uses and the availability of jobs at the zone of destination are trip attractors. The use of a route planner offers a novel approach to modelling and understanding bicycling flows within a city. This may be useful for transportation planners to infer different types of routes that bicyclists may seek out and consider these in travel demand models.
A fully separated bicycle network from vehicular traffic is not realistic even for the most bicycle-friendly cities. Thus, all around the world urban cycling entails switching between streets of different safety, convenience, and comfort levels. As a consequence, the quality of bicycle networks should be evaluated not based on one but multiple factors and by considering the different user preferences regarding these factors. More comprehensive methodologies to assess urban bicycle networks are essential to the operation and planning of modern city transportation. This work proposes a multi-objective methodology to assess—what we refer to as—bikeability between origin–destination locations and over the entire network, useful for evaluation and planning of bicycle networks. We do so by introducing the concept of bikeability curves which allows us to assess the quality of cycling in a city network with respect to the heterogeneity of user preferences. The application of the proposed methodology is demonstrated on two cities with different bike cultures: Amsterdam and Melbourne. Our results suggest the effectiveness of bikeability curves in describing the characteristic features and differences in the two networks.
Automobiles are central to participation in economic, social, and cultural activities in the United States. The ability to drive as one ages is fundamental to the quality of life among older adults. Driving rates decline significantly with age. Researchers using cross-sectional data have studied the reasons former drivers have stopped driving, but few have followed individuals over time to examine changes in relationships among driving cessation, socio-demographics, and health conditions. We used longitudinal data from a national sample of 20,000 observations from the University of Michigan Health and Retirement Study (HRS) to examine relationships among demographic variables, health conditions, and driving reduction and driving cessation. Longitudinal data allow analysis of generational differences in behavior, a major advantage over cross-sectional data which only allow comparisons of different people at one point in time. We found, like many other studies, that personal decisions to limit and eventually stop driving vary with sex, age, and health conditions. In addition, unlike most previous studies, we also found that those relationships differ by birth cohort with younger cohorts less likely to stop and limit their driving than their older counterparts. The findings indicate an evolution in the association between driving cessation and its causes.
In recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.
More and more cities worldwide are striving for sustainability and livability. Measuring the service or performance of local-scale spaces for pedestrians and bicyclists to better understand how to provide “walkable” and “bikeable” environments is key in this endeavor to enhance active transportation. These pedestrian and bicycle service or performance indicators, such as Level of Traffic Stress or Level of Service, relate measurable characteristics with a perceived proxy of the performance or service, such as comfort, satisfaction, or quality of service (QoS). The purpose of this study is to propose and validate a framework that integrates user-oriented inputs to the existing traditional supply-oriented variables to explain the QoS in segment roadways in urban environments for active modes. The conceptual framework underlying this study considers the contribution of individual perceptions, in addition to the traditionally considered operational and geometry variables, to explain the perceived QoS of pedestrian and bicyclist infrastructure. The framework is tested via two separate and independent surveys for pedestrians and bicyclists. Evidence determined the relative importance of these supply-oriented and user-oriented factors to explain the QoS. The superior explanatory power of the perception variables and in terms of the variables that explain the individuals’ perceived QoS justify the framework for both pedestrians and bicyclists.
Ride-hailing (RH) services have been growing rapidly and gaining popularity worldwide. However, many transit agencies are experiencing ridership stagnation or even decline. Understanding the correlation between RH trips and transit ridership has become an urgently important matter for transit agencies. This study aimed to explore the relationship between RH and public transit ridership and provide a starting point for future studies. This study benefitted from having access to detailed data on trip-level RH trips, transit supply and transit ridership in Toronto for three years (2016–2018). With this dataset, the study utilized random-effects panel data models and log–log regression models to estimate the correlation of RH pickup/drop-off counts with subway station and surface transit route (buses and streetcars) ridership within transit catchment areas, broken down into five different periods of a non-summer weekday. The results show that RH services generally have a positive association with subway station ridership while negatively correlating with surface transit route ridership. The positive relationship between RH and subway station ridership is the strongest during the mid-day and early evening. In contrast, the negative relationship between surface transit routes and RH ridership is the highest during peak commuting hours. Additionally, RH trip volume is more positively related to ridership at terminal/transfer subway stations in Toronto’s city centre while more negatively associated with routes with relatively poor services (e.g., low on-time performance, low vehicle running speed and low frequency) in the city centre where traffic congestion can be severe. According to the above findings, the degree of the relationship between RH and public transit demand tends to be mixed, varying by transit mode, time of day and transit level-of-service. The gained knowledge about RH and transit can provide insights for transit agencies to improve transit services, which are discussed in this paper.
For an increasing number of cities, managing tourism becomes an important task and accordingly better understanding of touristic travel patterns is required. We model the sightseeing-tour choice within a city as a utility maximization problem. For this, attractions and their intrinsic utilities as well as tourists’ preferences are evaluated over multiple dimensions in order to explain the variance in tourists’ choice of POIs (points of interest) including the visiting order. Furthermore, the choice of destinations is considered “history-dependent” in that there is diminishing marginal utility gained by visiting additional POIs. Given the many potential sights, this leads to a large combinatorial problem. We solve this with a variant of a TTDP (tourist trip design problem) with the modified distance that evaluates omitted POIs and geographical distance between estimated and observed tours. The approach is applied to revealed-preference survey data from Kyoto, Japan, where tourists stated their visited attractions among 37 touristic areas. We discuss model fit and scenarios with the existing and a modified transport network.
Web-based stated preference (SP) surveys are widely used to estimate values of travel time (VTT) for cost–benefit analysis, often with internet panels as the source of recruitment. The recruitment method could potentially bias the results because (1) those who frequently participate in surveys may have a lower opportunity cost of time and (2) people who answer the survey at home or in the office may answer differently because the choice situation is less salient to them. In this paper, we investigate both mechanisms using data from a VTT choice experiment study where respondents were recruited from an internet panel, an alternative email register or on-board/on the station. Within all three groups, some complete the survey while making an actual trip. We find that respondents who were recruited from the internet panel or report being members of a panel have a significantly lower VTT, suggesting that internet panels are less representative in this respect compared to other recruitment methods. We also find that those who answer while traveling have a higher VTT, possibly because the benefits of saving travel time are more salient to them than to those who answer while not traveling.
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.
Promoting sustainable transportation, ride-sourcing and dynamic ridesharing (DRS) services have transformative impacts on mobility, congestion, and emissions. As emerging mobility options, the demand for ride-sourcing and DRS services has rarely been simultaneously examined. This study contributes to filling this gap by jointly analyzing the demand for ride-sourcing and DRS services and examining how it varies across neighborhood-level built environment, transit accessibility and crime, behavioral, and sociodemographic factors. To achieve these objectives, unique geo-coded data containing millions of ride-sourcing and DRS trips in Chicago are spatially joined with up-to-date data on the built environment, transit accessibility, crime, active travel, and demographic factors. A novel Markov Random Field-based joint heterogeneous geo-additive copula framework is presented to simultaneously capture random, systematic, and spatial heterogeneity. Characterized by a Frank copula structure, the demand for ride-sourcing and DRS services exhibited a non-linear stochastic dependence pattern. With spatial heterogeneity and spillover effects, the stochastic dependence of ride-sourcing and DRS demand varied across time of day and was the strongest in compact and dense neighborhoods. Key aspects of the built environment related to urban design (pedestrian-oriented infrastructure), density, and land-use diversity were positively associated with ride-sourcing and DRS demand—suggesting that sustainable mobility goals can be achieved by continuing to invest in more walkable neighborhoods. Active travel and telecommuting were positively linked with ride-sourcing and DRS demand. Complementary and substitutive effects for transit accessibility were found. Results show that increasing transit accessibility in areas with low levels of accessibility (compared to those with high transit levels) could be more helpful in increasing the adoption of ride-sourcing and DRS services. Relative to ride-sourcing, the demand for DRS services appeared more responsive to improvements in pedestrian-infrastructure and transit accessibility. Quantification of non-linear associations with ceiling and overdose effects for the built environment, vehicle ownership, and transit accessibility provided deeper insights. The findings can help guide the development of policy interventions and investment decisions to further accelerate the adoption of mobility-on-demand systems.
A high-speed rail (HSR) system, which can be developed either by building a new segregated line or upgrading an existing line according to a given set of operational standards, is considered as a competitive solution to improve the accessibility of main destinations. Scientific literature has reported limited contributions regarding the impacts of such infrastructures on the regional systematic mobility and their negative effects on locations excluded from the service. To fill this gap, this paper proposes a method for assessing the implications of regional accessibility on work and study trips, by comparing the two HSR options mentioned above (new segregated or upgraded existing lines). Instead of considering static indicators (e.g., population), the number of train commuters and the variation in travel times for each of the local employment systems crossed by the railway are used as input data. This method is then applied to analyse the territories located along the Venice–Trieste line (in the north-eastern part of Italy) that are characterised by several medium-sized municipalities and crossed by two TEN-T lines. An upgrade of the existing line rather than the construction of a segregated HSR is preferable for local commuters in terms of average travel times and social equity, also considering the expected construction costs. These results complement traditional medium- and long-distance market analyses and may be useful for policymakers to define the most appropriate territorial strategies for the development of specific TEN-T stretches.
The choice of freight vehicle type and shipment size are among the most important logistics decisions made by firms. An important aspect is the nature of the choice process i.e., whether the two choices are sequential or joint in nature. In this study, we investigate the factors that influence the two choices and develop sequential and nested logit models with both possible sequence or nesting structures i.e., vehicle type first or at upper level and shipment size second or at lower level and vice-versa. A commercial travel survey for the Greater Toronto and Hamilton Area is used to estimate the models. Characteristics of firms including industry type and employment, and characteristics of shipments including commodity type, destination location, and density value are tested. Shipment size is categorized into four categories and four vehicle types are considered. The results show that both sequences and nesting structures are possible. The nested logit model results show a potential correlation among unobserved components of utility for vehicle types (80%) and shipment sizes (38%) which should be considered. Model performance is assessed using rho-squared and BIC value. The results show that the sequential logit model with shipment size first and vehicle type second sequence has the best model fit. However, based on the strong correlation indicated by the nested logit model for vehicle type nested within shipment size choice (second best model), a model reflecting the joint nature of the choice process might be suitable.
Commuting is expensive in megacities of emerging economies. By decreasing work-related trips, teleworking may reduce congestion and commuting time. Taking Mexico City’s office workers’ as case study, this paper reports findings from a discrete choice experiment (DCE) exploring willingness to see a cut in monthly paycheck in exchange for teleworking two days a week from a shared office. This DCE explores preferences for bike parking spaces at shared office’s facilities, and walking commuting time to shared office. This design allows estimation of willingness to pay (WTP) for teleworking across commuting time scenarios. Monthly WTP for teleworking 2 days a week starts at (2019) USD 76.68—if commuting time is zero. As 1 h of commuting time is valued at USD 61.97 on a monthly basis, WTP for teleworking 30 min away from home is USD 45.69. Wealthier respondents report higher value for commuting time and WTP for teleworking. Monthly value of bike parking infrastructure is USD 14.70—reaching USD 30.98 for commuters that walk or (motor-)bike less than 50 min. We illustrate how these stated benefits can inform cost-benefit analysis of transportation, housing, and labor policies that enable teleworking and/or reduce commuting times in Mexico City.
Machine learning (ML) architecture has successfully characterized complex motorized vol- umes and travel patterns; however, non-motorized traffic has given less attention to ML techniques and relied on simple econometric models due to a lack of data for complex modeling. Recent advancements in smartphone-based location data that collect and pro- cess large amounts of daily bicycle activities makes the use of machine learning techniques for bicycle volume estimations possible and promising. This study develops eight mod- eling techniques ranging from advanced techniques, such as Convolution Neural Network (CNN), Deep Neural Network (DNN), Shallow Neural Network (SNN), Random Forest (RF), XGBoost, to conventional and simpler approaches, such as Decision Tree (DT), Negative Binomial (NB), and Multiple Linear Regression, to estimate Daily Bicycle Traf- fic (DBT). This study uses 6746 daily bicycle volumes collected from 178 permanent and short-term count locations from 2017 to 2019 in Portland, Oregon. A total of 45 independ- ent variables capturing anonymous bicycle user activities (Strava count, bike share), built environments, motorized traffic, and sociodemographic characteristics create comprehen- sive variable sets for predictive modeling. Two variable dimension reduction techniques using principal component analysis and random forest variable importance analysis ensure that the models are not over-generalized or over-fitted with a large variable set. The com- parative analysis between models shows that the SNN and DNN machine learning tech- niques produce higher accuracies in estimating daily bicycle volumes. The results show that the DNN models predict the DBT with a maximum mean absolute percentage error (MAPE) of 22% while the conventional model (linear regression) shows an APE of 45%.
Urban rail transit often operates with high service frequencies to serve heavy passenger demand during rush hours. Such operations can be delayed by two types of congestion: train congestion and passenger congestion, both of which interact with each other. This delay is problematic for many transit systems, since it can be amplified due to the interaction. However, there are no tractable models describing them; and it makes difficult to analyze management strategies of congested transit systems in general and tractable ways. To fill this gap, this article proposes simple yet physical and dynamic model of urban rail transit. First, a fundamental diagram of transit system (i.e., theoretical relation among train-flow, train-density, and passenger-flow) is analytically derived considering the aforementioned physical interaction. Then, a macroscopic model of transit system for dynamic transit assignment is developed based on the fundamental diagram. Finally, accuracy of the macroscopic model is investigated by comparing to microscopic simulation. The proposed models would be useful for mathematical analysis on management strategies of urban rail transit systems, such as optimal dynamic pricing for travel demand management.