To read the full-text of this research, you can request a copy directly from the author.
The market potential of a fleet of shared autonomous electric vehicles
(SAEVs) is explored by using a multinomial logit mode choice model in
an agent-based framework and different fare settings. The mode share
of SAEVs in the simulated midsize city (modeled roughly after Austin,
Texas) is predicted to lie between 14% and 39% when the SAEVs compete
with privately owned, manually driven vehicles and city bus service.
The underlying assumptions are that SAEVs are priced between $0.75/mi
and $1.00/mi, which delivers significant net revenues to the fleet owner–
operator under all modeled scenarios; that they have an 80-mi range and
that Level 2 charging infrastructure is available; and that automation
costs are up to $25,000 per vehicle. Various dynamic pricing schemes
for SAEV fares indicate that specific fleet metrics can be improved with
targeted strategies. For example, pricing strategies that attempt to balance
available SAEV supply with anticipated trip demand can decrease
average wait times by 19% to 23%. However, trade-offs exist within this
price setting: fare structures that favor higher revenue-to-cost ratios—
by targeting travelers with a high value of travel time (VOTT)—reduce
SAEV mode shares, while those that favor larger mode shares—by
appealing to a wider VOTT range—produce lower payback.
To read the full-text of this research, you can request a copy directly from the author.
... Therefore, planning studies also focus on interactions between AMoD systems and existing infrastructure. Aspects of interest include allocation of parking space (48,49), optimal placement of charging stations, charge scheduling for electric AVs (50,51,52,53,54), contribution to congestion (55), joint operation with other modes of transportation (56,57), autonomous calibration (58), smart infrastructure (59), and achievable performance of the overall mobility system (45). ...
... Interactions among stakeholders AMoD systems are part of a large ecosystem of mobility providers which includes public transportation systems and private companies. Importantly, studies investigate the interplay between AMoD systems, other mobility solutions, and regulatory agencies (e.g., municipalities) (60, 61), focusing on pricing strategies, incentives, and tolls (51,62,63,64,65). While constituting an exciting research direction, the study of interactions among stakeholders is not the focus of this article. ...
... The study of elastic demand for AMoD systems is therefore a natural direction for future research. This is an interdisciplinary research area, touching upon topics such as incentives, customer preferences, and mode choice (51,63,99,159,160). Questions of interest include: how can AMoD systems attract new customers? ...
Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms. In this article, we present the emerging concept of Autonomous Mobility-on-Demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to Autonomous Mobility-on-Demand systems. Specifically, we first identify problem settings for their analysis and control, both from the operational and the planning perspective. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.
... Despite intense scholarly interest in the sharing economy, the nexus between the sharing economy and environmental degradation has not yet undergone a comprehensive review. The existing literature has examined the impact of the sharing economy on the environment, either for specific industries (  for bike sharing;  for car sharing) or for specific regions (  for Switzerland;  for the United States;  for China). Although a micro-level analysis of the sharing economy is interesting, it lacks the ability to make a comparison across countries. ...
... Cao and Shen  show that there is a negative relationship between the use of bike sharing and CO2 emissions in Beijing, where the riding distance of shared bikes is one of the most important factors behind the reduction of emissions. Moreover, Chen and Kockelman  show that sharing practices have reduced transportation energy use and greenhouse gas emissions by 51% in the United States. For Europe, Rydén and Morin  document that car sharing can lead an average decrease of 3000 km per year, and a 40%-50% decrease in CO2 and 28% and 45% reduction in car use in Brussels and Bremen, respectively. ...
... The findings reveal that while a high sharing economy level is negatively associated with CO2 emissions, it is positively associated with overall environmental performance. Using cross-sectional analysis, the results lead us to conclude that sharing activities help to improve the environment more than the negative effect from additional consumption stimulated by the sharing economy practices [9,10,30,41]. Our analysis further suggests that, while secondary industries and population density have a negative impact on environmental performance, high income, education, and trade freedom improve the quality of the environment. ...
Using cross-sectional data from 165 countries, this study takes a fresh look at whether or not the sharing economy is a green solution for countries. This study relies on the Timbro sharing economy index and uses both carbon emission and environmental performance index as proxies for the greenhouse gas effect and overall environment, respectively. Due to limited sample size and non-normal distribution of the sample, this paper applies the Bayesian regression model, which is based on posterior distribution. The findings suggest the following: (1) a high sharing economy level has a negative relationship to carbon emissions but a positive relationship to overall environmental performance; (2) the joint variables show that a high sharing economy level together with high broadband access, urbanization, and high education level reduces carbon emissions; and (3) for manufacturing countries, a high sharing economy level together with high urbanization is associated with comparatively low carbon emissions and high environmental performance. In general, these findings allow us to conclude in favor of the contribution made by a high sharing economy level to sustainability.
... Some aim to capture the temporal elasticity of demand to provide optimal solutions for a specific objective (e.g., profit maximization) (Sayarshad and Chow, 2015;Qian and Ukkusuri, 2. Background and Related Literature 2017). Some try to improve the reliability of the proposed solution by considering the spatial heterogeneity of the level of demand over the network (Chen and Kockelman, 2016;Guo et al., 2017;Qiu et al., 2018;Bimpikis et al., 2019). Furthermore, the users heterogeneity which is represented by passenger preference/behavioral models is also an aspect that has been heavily researched in the literature (Chen and Kockelman, 2016;Qiu et al., 2018;Guan et al., 2019). ...
... Some try to improve the reliability of the proposed solution by considering the spatial heterogeneity of the level of demand over the network (Chen and Kockelman, 2016;Guo et al., 2017;Qiu et al., 2018;Bimpikis et al., 2019). Furthermore, the users heterogeneity which is represented by passenger preference/behavioral models is also an aspect that has been heavily researched in the literature (Chen and Kockelman, 2016;Qiu et al., 2018;Guan et al., 2019). Note that, some dynamic pricing methods are only applicable in specific operation context, e.g., in the monopoly (Qiu et al., 2018;Bimpikis et al., 2019), or duopoly (Sato and Sawaki, 2013). ...
... The Multinomial Logit (MNL) model was applied to estimate the mode share of shared autonomous electric vehicles (SAEV) in an agent-based framework in Chen and Kockelman (2016). It investigated the trade-offs between the revenue and mode share of SAEV under different pricing schemes including distance-based pricing, origin-based pricing, destinationbased pricing, and combination pricing strategy. ...
Ride-sharing has become ubiquitous in many metropolises attributed to its affordability, convenience and flexibility. This thesis will develop dynamic pricing methods for ride-sharing services to improve its competitiveness in multi-modal transportation systems.
A market equilibrium model for ride-sharing services with a consideration of passenger preference in a multi-modal transportation system is built at the network level where network structure and origin-destination (OD) demand pattern are explicitly counted. Moreover, the method to calculate the system endogenous variables (e.g., ride-sharing demand, expected waiting time, and expected detour time) in the equilibrium is also deduced. A stated-preference survey data regarding the mode choice within private car, public transport and ride-sharing services are utilized to estimate the passenger preference. To reveal the operation difference of ride-sharing in different scale networks, a handmade network and the Munich network are adopted in the experiments.
We propose three different pricing strategies: 1) a unified pricing method (trip fare is a function of travel distance with the same unit price for all OD pairs over the network); 2) a spatial pricing method accounting for the spatial heterogeneity of the level of demand over the network by applying different unit prices for different OD pairs; 3) a utility-based compensation method compensating passengers based on their travel experiences to reduce variance/uncertainty for trip level-of-services (LOS) and add equity with or without (limited) sacrifice of the operation objectives. Gradient Descent (GD) algorithms are derived to optimize the operation strategy (trip fare and vehicle fleet size) for the monopoly optimum (MO) scenario and social optimum (SO) scenario for method 1 and 2, respectively. For method 3, a heuristic particle swarm algorithm (PSO) is applied. The results show that, in method 1, the optimal unit price for MO is greater than that for SO, while the optimal vehicle fleet size is smaller. And the difference between optimal vehicle fleet sizes in two scenarios becomes greater in the high demand level situation, while the difference between optimal unit prices almost keeps the same. In method 2, it is found that the optimal unit prices are linear to their distance and ride-sharing demand with negative slopes. Last but not least, the results illustrate that method 3 is effective to improve the LOS and equity without losses of profit or surplus as expected.
... Simply put, having SDVs at their disposal is assumed to enable people to travel more, both longer distances and for longer periods of time. For example, the value of in-vehicle travel time in a shared electric SDV is modeled at 35% of the in-vehicle travel time in a non-self-driving privately owned vehicle which is said to be equivalent of travel time as in (rail-based) public transport (Chen and Kockelman 2016). To a certain extent, the lower VTT for SDVs has also been confirmed by current empirical evidence (Cyganski, Fraedrich, and Lenz 2015). ...
... To address this, some authors compare stated preference for SDVs with actual usage of chauffeur driven cars (Wadud and Huda 2019). While some work relates to shared SDVs (Chen and Kockelman 2016), the actual characteristics of the travel mode do not seem to be taken into consideration. For example, though the notion of crowding does not apply in shared SDVs, one of the suggested drivers of the dissatisfaction does, namely the physical closeness of other travelers per se (Haywood, Koning, and Monchambert 2017). ...
Understanding the value of travel time for mobility concepts based on self-driving vehicles is crucial to accurately value transport investments and predict future travel patterns. In this paper, we carry out a morphological analysis to illustrate the diversity of mobility concepts based on self-driving vehicles and the complexity of determining the value of travel time for such concepts. We consider four categories of parameters that directly or indirectly impact the value of travel time: (i) vehicle characteristics, (ii) operating principles, (iii) journey characteristics and (iv) traveler characteristics. The parameters and respective attributes result in a morphological matrix that spans all possible solutions. Out of these, we analyze five plausible solutions based on the implications of the concept characteristics on the total value of travel time. We conclude by suggesting an alternative approach to differentiate value of travel time based on travel characteristics rather than the usual decomposition into transport modes.
... Certain rules, heuristics, or precise optimization algorithm are three main methods to solve this problem. For modeling dynamic process of vehicle assignment, a rulebased vehicle assignment method is usually implemented [26,28,. e most widely used rule is assigning the nearest vehicle to the user request. ...
... Charging and parking assignment refers to monitoring real-time battery levels of SAEVs and conducting corresponding strategies to assign vehicles to charging piles or parking lots [34,52]. Regarding charging assignment, Chen et al.  insisted that the charging vehicles are not allowed to undock and serve a new trip request but Bauer et al.  believed that still-charging vehicles are allowed to serve a new trip request. Iacobucci et al. [34,53] developed a simulation methodology for evaluating a Shared Autonomous Electric Vehicle system interacting with passengers and charging at designated charging stations using a heuristic-based charging strategy and used electricity price information for optimizing vehicle charging in a mixed-integer optimization model by adding charging constraints over longer time scales. ...
The fusion of electricity, automation, and sharing is forming a new Autonomous Mobility-on-Demand (AMoD) system in current urban transportation, in which the Shared Autonomous Electric Vehicles (SAEVs) are a fleet to execute delivery, parking, recharging, and repositioning tasks automatically. To model the decision-making process of AMoD system and optimize multiaction dynamic dispatching of SAEVs over a long horizon, the dispatching problem of SAEVs is modeled according to Markov Decision Process (MDP) at first. Then two optimization models from short-sighted view and farsighted view based on combinatorial optimization theory are built, respectively. The former focuses on the instant and single-step reward, while the latter aims at the accumulative and multistep return. After that, the Kuhn–Munkres algorithm is set as the baseline method to solve the first model to achieve optimal multiaction allocation instructions for SAEVs, and the combination of deep Q-learning algorithm and Kuhn–Munkres algorithm is designed to solve the second model to realize the global optimization. Finally, a toy example, a macrosimulation of 1 month, and a microsimulation of 6 hours based on actual historical operation data are conducted. Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.
... SAV simulation efforts are usually made for door-to-door service, around fleet sizing decisions , ridesharing mechanisms , electric vehicles involving charging decisions [17,18], and environmental effects [2,12]. Spieser et al.  investigated the proper fleet size in Singapore that could serve the travel demand while ensuring a desired level of service. ...
Before shared automated vehicles (SAVs) can be widely adopted, they are anticipated to be implemented commercially in confined regions or fixed routes where the benefits of automation can be realized. SAVs have the potential to operate in a traditional transit corridor, replacing conventional transit vehicles, and have frequent interactions with riders and other vehicles sharing the same right of way. This paper microsimulates SAVs’ operation on a 6.5-mile corridor to understand how vehicle size and attributes of such SAV-based transit affect traffic, transit riders, and system costs. The SUMO (Simulation of Urban MObility) platform is employed to model microscopic interactions among SAVs, transit passengers, and other traffic. Results show that the use of smaller, but more frequent, SAVs leads to reduced passenger waiting times but increased vehicle travel times. More frequent services of smaller SAVs do not, in general, significantly affect general traffic due to shorter dwell times. Overall, using smaller SAVs instead of the large 40-seat SAVs can reduce system costs by up to 4% while also reducing passenger waiting times, under various demand levels and passenger loading factors. However, the use of 5-seat SAVs does not always have the lowest system costs.
... Some aimed to understand AVs' impacts on traffic flow and infrastructure design . Others have examined how AVs might affect travel demand and future transportation forecasts; for example, some have focused on AVs' effects on various aspects of travel behavior (e.g., vehicle ownership, vehicle miles traveled, travel frequency), including trip generation , travel mode choice  and overall condition with four-step, activity-based, or other simulation models [5,6,31,36]. ...
Autonomous vehicles (AVs) may significantly impact people’s choice of residential locations and spatial structures. The impact may vary across different countries, but few studies have focused on it. This study drew on China and the United States (US) as two cases to study car drivers’ knowledge of AVs and willingness to move farther if AVs were available by estimating ordered logistic regression models. The results showed that 42.3% of Chinese and 29.8% of US respondents were likely to consider moving farther away from the nearest city or the destination for the most frequent trip if they had an AV. The Chinese sample had less knowledge of AVs than the US sample, but they were more likely to consider a move. AVs may lead to a new round of urban sprawl, but the challenge may be greater for China. We captured the socio-economic and transport factors that affected this result.
... Zhang et al.  analyzed the generalized cost for autonomous buses, which is one of the key elements for analyzing passenger preferences in using autonomous buses. Chen and Kockelman  explored the impact of pricing strategies on the market share of shared autonomous electric vehicles. ...
Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.
... • Shared AV systems, which are similar to a taxi system, ride-hailing systems of transportation network companies, or on-demand microtransit systems (Fagnant and Kockelman 2014;2018;Azevedo et al. 2016;Chen and Kockelman 2016;Farhan and Chen 2018). • AVs as first-mile and last-mile services, which feed and pick up passengers to and from transit stations along corridors served by high-capacity transit such as bus rapid transit and urban rail transit (Chong et al. 2011;Moorthy et al. 2017;Shen, Zhang, and Zhao 2018;Scheltes and de Almeida Correia 2017). ...
Previous surveys of public attitudes toward automated vehicle (AV) and transit integration primarily took place in large urban areas. AV-transit integration also has a great potential in small urban areas. A survey of public attitudes towards AV-transit integration was carried out in two small urban areas in Wisconsin, United States. A total of 266 finished responses were analyzed using text mining, factor analysis, and regression analysis. Results showed that respondents knew about AVs and driving assistance technologies. Respondents welcome AV-transit integration but were unsure about its potential impacts. Technology-savvy respondents were more positive but had more concerns about AV-transit integration than others. Respondents who enjoyed driving were not necessarily against transit, as they were more positive about AV-transit integration and were more willing to use automated buses than those who did not enjoy driving as much. Transit users were more positive toward AV-transit integration than non-transit users.
... Main application areas include optimization of pricing strategies for MSPs - , analysis of interactions between MSP and users - , interactions between authorities and MSPs - , and tolls and incentives to regulate congested networks - . While ,  use game theory to determine prices for public transport, ,  focus on subsidies and management of shared fleets of electric vehicles, and ,  focus on pricing strategies at the network level. The competition among MSPs is studied in  through a realtime gaming framework, in  focusing on AMoD systems, and via evolutionary game theory with a focus on ridesourcing in . ...
Increasing urbanization and exacerbation of sustainability goals threaten the operational efficiency of current transportation systems and confront cities with complex choices with huge impact on future generations. At the same time, the rise of private, profit-maximizing Mobility Service Providers leveraging public resources, such as ride-hailing companies, entangles current regulation schemes. This calls for tools to study such complex socio-technical problems. In this paper, we provide a game-theoretic framework to study interactions between stakeholders of the mobility ecosystem, modeling regulatory aspects such as taxes and public transport prices, as well as operational matters for Mobility Service Providers such as pricing strategy, fleet sizing, and vehicle design. Our framework is modular and can readily accommodate different types of Mobility Service Providers, actions of municipalities, and low-level models of customers choices in the mobility system. Through both an analytical and a numerical case study for the city of Berlin, Germany, we showcase the ability of our framework to compute equilibria of the problem, to study fundamental tradeoffs, and to inform stakeholders and policy makers on the effects of interventions. Among others, we show tradeoffs between customers satisfaction, environmental impact, and public revenue, as well as the impact of strategic decisions on these metrics.
... For further extensive reviews of the plausible impacts of automated vehicles, McGehee et al. (2016), Milakis et al. (2017), Innamaa et al. (2017) Although state-of-the-art research studies are very compelling, in reality we hardly know what is really going to happen when self-driving vehicles are deployed. Most of the literature on use cases for self-driving vehicles is heavily biased towards transportation systems based on (shared) autonomous taxis, and their performance as an alternative to private car usage (e.g., Chen and Kockelman, 2016;Meyer et al., 2017;OECD, 2015), or routing strategies for such systems (e.g., Han et al., 2016;Suganuma and Yamamoto, 2016). Literature on self-driving vehicles in public transport is even more limited, and provides primarily two types of applications. ...
... Car sharing may also save resources by avoiding the production of new cars, increasing the utilisation rate, allowing users to choose the right car size , and reducing the need for parking spaces . However, it could also be used as an addition to private cars  and public transportation . ...
By changing the institutionalised practices associated with resource distribution, the sharing economy could support sustainable urban transformations. However, its impacts on urban sustainability are unknown and contested, and key actors hold different perceptions about them. Understanding how they frame these impacts could help solve conflicts and outline what can be done to influence the development of the sharing economy in a way that fosters urban sustainability. This study explores the diversity of these frames across actors (sharing economy organisations and municipalities), segments (accommodation, bicycle, and car sharing), and cities (Amsterdam and Toronto). A framework of the impacts on urban sustainability was developed following a systematic literature review. This then guided the analysis of secondary data and 51 interviews with key actors. Results show that accommodation sharing is framed most negatively due to its impact on urban liveability. Bicycle sharing is surrounded by less conflict. Still, in Amsterdam, which has a well-functioning bicycle infrastructure, it is viewed less positively than in Toronto. Car sharing is the most positively framed segment in Amsterdam as its potentials to lower emissions align with municipal sustainability agendas. Practical insights for negotiations between sharing economy organisations and municipalities to advance urban sustainability are proposed.
... For instance, when profit is the most prominent driving force, equity concerns may occasionally be sidelined. As shown in , some very profitable strategies perpetually ignore upcoming demands that are less lucrative. ...
Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Due to supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately since service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles (AVs), however, the characteristics of mobility services change, and new opportunities to overcome the prevailing limitations arise. This thesis proposes a series of learning- and optimization-based strategies to help autonomous transportation providers meet the service quality expectations of diversified user bases. We show how autonomous mobility-on-demand (AMoD) systems can develop to revolutionize urban transportation, improving reliability, efficiency, and accessibility.
... However, energy use and GHG emissions depend on several aspects such as ridership characteristics, infrastructure, operational practices, and propulsion technology. Several authors have considered the use of electric vehicles in ondemand mobility service offerings, e.g., . Compared to internal combustion engine vehicles, they indicate that electric vehicles lead to decreased energy use and environmental pollution. ...
This study examines the effects of on-demand mobility services on sustainability in terms of emissions and traffic volume. According to our simulations, implementing on-demand mobility services is recommendable only as a supplement to public transport in both urban and rural regions since there are positive effects in terms of CO2 emissions. However, in urban areas, there is a negative impact on the traffic volume in terms of additional vehicle kilometres since the bundled public transport demand is replaced by less bundled on-demand vehicles. In rural areas, the increase in vehicle kilometres plays less of a role due to generally low demand. The negative effects per vehicle kilometre are slightly higher in rural areas due to higher empty kilometres and lower bundling rates, but the negative effects per km2 in dense cities are much more serious. Authorities need to consider these effects according to the spatial context when implementing such services.
... More recently, during trials of Although all these studies to date are very compelling, in reality, we know very little about what will actually happen when self-driving vehicles are deployed. Most of the literature on use cases for selfdriving vehicles is heavily biased towards transport systems based on autonomous taxis and their performance compared to private car usage (e.g., OECD, 2015;Chen and Kockelman, 2016;Meyer et al., 2017). Based on the great uncertainties mentioned above, we believe it is extremely important for researchers to reflect on observations from real-world deployments of this technology. ...
The sustainable and continuous development of public transport systems is crucial to ensuring robust and resilient transport and economic activity whilst improving the urban environment. Through technological improvement, cities can increase the competitiveness of public transport, promote equality and pursue a multi-modal shift to greener solutions. The introduction of vehicle automation technology into existing public transport systems has potential impacts on mobility behaviours and may replace conventional bus service in the future. This study examines travellers’ preferences for automated buses versus conventional buses, using a context-dependent stated choice experiment. This experiment measured the effects of context variables (such as trip purpose, travel distance, time of day, weather conditions and travel companion) on the choice of automated buses versus conventional buses. The results were analysed using mixed logit models, and the findings indicate that, in general, choice behaviours do not diverge much between the choice of automated bus and conventional bus. However, individuals’ choices are more elastic towards the changes in automated bus service levels compared to conventional bus service. The results show that poor weather conditions may lower the quality and reliability of public transport service, and the probability of choosing an automated bus over a conventional bus is reduced due to such disruptions. In addition, passengers travelling for work purposes, covering long distances, or travelling with companions are more likely to choose conventional buses than automated buses.
... The importance of this problem from an operational efficiency perspective and transportation systems modeling perspective has motivated a sizable volume of research over the past five years. Several studies employ rule-based AV-traveler policies that mainly involve assigning new requests to the nearest idle AV Chen and Kockelman, 2016;Fagnant et al., 2015;Fagnant and Kockelman, 2014;Gurumurthy et al., 2020). These rule-based algorithms have been integrated into large-scale transportation network simulation models. ...
The goal of this paper is to develop a modeling framework that captures the inter-decision dynamics between mobility service providers (MSPs) and travelers that can be used to optimize and analyze policies/regulations related to MSPs. To meet this goal, the paper proposes a tri-level mathematical programming model with a public-sector decision maker (i.e. a policymaker/regulator) at the highest level, the MSP in the middle level, and travelers at the lowest level. The public-sector decision maker aims to maximize social welfare via implementing regulations, policies, plans, transit service designs, etc. The MSP aims to maximize profit by adjusting its service designs. Travelers aim to maximize utility by changing their modes and routes. The travelers’ decisions depend on the regulator and MSP’s decisions while the MSP decisions themselves depend on the regulator’s decisions. To solve the tri-level mathematical program, the study employs Bayesian optimization (BO) within a simulation–optimization solution approach. At the lowest level, the solution approach includes an agent-based transportation system simulation model to capture travelers’ behavior subject to specific decisions made by the regulator and MSP. At the middle and highest levels, the solution approach employs BO for the MSP to maximize profit and for the regulator to maximize social welfare. The agent-based transportation simulation model includes a mode choice model, a road network, a transit network, and an MSP providing automated mobility-on-demand (AMOD) service with shared rides. The modeling and solution approaches are applied to Munich, Germany in order to validate the model. The case study investigates the tolls and parking costs the city administration should set, as well as changes in the public transport budget and a limitation of the AMOD fleet size. Best policy settings are derived for two social welfare definitions, in both of which the AMOD fleet size is not regulated as the shared-ride AMOD service provides significant value to travelers in Munich.
... The importance of this problem from an operational efficiency perspective and transportation systems modeling perspective has motivated a sizable volume of research over the past five years. Several studies employ rule-based AV-traveler policies that mainly involve assigning new requests to the nearest idle AV Chen and Kockelman, 2016;Fagnant et al., 2015;Fagnant and Kockelman, 2014;Gurumurthy et al., 2020). These rule-based algorithms have been integrated into large-scale transportation network simulation models. ...
The goal of this paper is to develop a modeling framework that captures the inter-decision dynamics between mobility service providers (MSPs) and travelers that can be used to optimize and analyze policies/regulations related to MSPs. To meet this goal, the paper proposes a tri-level mathematical programming model with a public-sector decision maker (regulator) at the highest level, the MSP in the middle level, and travelers at the lowest level. The regulator aims to maximize social welfare via implementing regulations, policies, plans, transit service designs, etc. The MSP aims to maximize profit by adjusting its service designs. Travelers aim to maximize utility by changing their modes and routes. The travelers' decisions depend on the regulator and MSP's decisions while the MSP decisions themselves depend on the regulator's decisions. To solve the tri-level mathematical program, the study employs Bayesian optimization (BO) within a simulation-optimization solution approach. At the lowest level, the solution approach includes an agent-based transportation system simulation model to capture travelers' behavior subject to specific decisions made by the regulator and MSP. The agent-based transportation simulation model includes a mode choice model, a road network, a transit network, and an MSP providing automated mobility-on-demand (AMOD) service with shared rides. The modeling and solution approaches are applied to Munich, Germany in order to validate the model. The case study investigates the tolls and parking costs the city administration should set, as well as changes in the public transport budget and a limitation of the AMOD fleet size. Best policy settings are derived for two social welfare definitions, in both of which the AMOD fleet size is not regulated as the shared-ride AMOD service provides significant value to travelers in Munich.
... Due to the uncertainty of how the AV systems may evolve, many different scenarios of AV-PT interactions have been proposed (Lazarus et al., 2018). Some studies argue that AVs will compete with the PT systems (Levin and Boyles, 2015;Chen and Kockelman, 2016) or even replace them (Mendes et al., 2017), while others are optimistic about the AV-PT integration, stating that they could be complementary to each other (Lu et al., 2017;Wen et al., 2018). ...
Emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or compete with it. This study examines the competitive perspective where both AV and PT operators are profit-oriented with dynamic adjustable supply strategies under five regulatory structures regarding whether the AV operator is allowed to change the fleet size and whether the PT operator is allowed to adjust headway. Four out of the five scenarios are constrained competition while the other one focuses on unconstrained competition to find the Nash Equilibrium. We evaluate the competition process as well as the system performance from the standpoints of four stakeholders-the AV operator, the PT operator, passengers, and the transport authority. We also examine the impact of PT subsidies on the competition results including both demand-based and supply-based subsidies. A heuristic algorithm is proposed to update supply strategies for AV and PT based on the operators' historical actions and profits. An agent-based simulation model is implemented in the first-mile scenario in Tampines, Singapore. We find that the competition can result in higher profits and higher system efficiency for both operators compared to the status quo. After the supply updates, the PT services are spatially concentrated to shorter routes feeding directly to the subway station and temporally concentrated to peak hours. On average, the competition reduces the travel time of passengers but increases their travel costs. Nonetheless, the generalized travel cost is reduced when incorporating the value of time. With respect to the system efficiency, the bus supply adjustment increases the average vehicle load and reduces the total vehicle kilometer traveled measured by the passenger car equivalent (PCE), while the AV supply adjustment does the opposite. The results suggest that PT should be allowed to optimize its supply strategies under specific operation goals and constraints, and AV operations should be regulated to reduce their system impacts, including potentially limiting the number of licenses, operation time, and service areas, which makes AV operate in a manner more complementary to the PT system. Providing subsidies to PT results in higher PT supply, profit, and market share, lower AV supply, profit, and market share, and increased passengers generalized cost and total system PCE.
... Several studies have been conducted on various topics of AVs, such as sharing AVs (carsharing and ride-sharing), park-and-ride services , public transport [8,16,17], conceptual implementation , regional services [15,17], cost benefits [18,22,23], and placement of conventional vehicles . Previously, it has been reported that vehicle sharing [15,24,25] and ridesharing [21,26] profoundly affect AVs. Inspired by the pilot project CarLink [27,28], even the park-and-ride scheme was considered in the field of AVs . ...
In the context of global suburbanization and population aging, a low-speed, automated vehicle (LSAV) system provides essential mobility services in suburban residential areas. Although extensive studies on shared autonomous vehicle (SAV) services have been conducted, quantitative investigations on the operation of suburban LSAV systems are limited. Based on a demonstration pilot project of an autonomous vehicle called “Slocal Automated Driving”, we investigated the performance of an SAV system considering several scenarios in Kozoji Newtown, a suburban commuter town in Japan. The agent-based simulation results revealed that 40 LSAVs can satisfy the demands of 2263 daily trips with an average wait time of 15 min. However, in the case of a high-speed scenario, the same fleet size improved the level of service (LOS) by reducing the average wait time to two and a half minutes and halving the in-vehicle time. By contrast, the wait time in terms of the average and 95th percentile of the no-sharing ride scenario drastically deteriorated to an unacceptable level. Based on the fluctuations of hourly share rates, wait times, and the number of vacant vehicles, we determined that preparing for the potential fleet insufficiency periods from 7:00–13:00 and 15:00–18:00 can improve the LOS.
... La réduction de prix permise par le partage n'est donc pas un élément suffisant pour un report notable vers ce mode (Hörl, 2017 (Burns et al., 2013 ;Spieser et al., 2014 ;Fagnant et Kockelman, 2014 ;Martinez et Crist, 2015 ;Bösch et al., 2016 ;Bischoff et Maciejewski, 2016 ;Fagnant et Kockelman, 2016 ;Loeb et Kockelman, 2017 ;Dia et Javanshour, 2017 ;Gurumurthy et Kockelman, 2018 Lazarus et al., 2018 ;Basu et al., 2018). La majorité des travaux de simulation concluent ainsi à une baisse de la fréquentation des TC (de 3 à 60%), mais également souvent à une baisse de la part modale de la voiture particulière (20 à 48%) (Kröger et al., 2018 ;Levin et Boyles, 2015;Chen et Kockelman, 2016 ;Hörl et al., 2016 ;Bösch et al., 2017). Toutefois, une des seules enquêtes considérant une introduction des modes automatiques partagés et personnels simultanément estime une baisse de 6% de la part modale des transports collectifs, et une stabilité de la part modale voiture particulière (Pakusch et al., 2018). ...
Face à l’engouement suscité par le « véhicule autonome » depuis le milieu des années 2010, les pouvoirs publics locaux cherchent à préciser leur position et l’action publique à mener à moyen et long terme en la matière. En effet, les effets attendus de cette technologie, positifs, sur la sécurité et la circulation routières, mais aussi négatifs, sur le nombre de véhicule-kilomètres parcourus ou la fréquentation des transports en commun, s’inscrivent à l’échelle territoriale. Considérant l’incertitude inhérente à ces prévisions, le recours à l’évaluation ex ante est d’intérêt pour renseigner la décision publique locale. Cette thèse propose donc un cadre méthodologique pour une évaluation prospective et durable du véhicule autonome à l’échelle métropolitaine. La méthodologie définie est illustrée grâce à une « preuve de concept », consistant en une application à la Métropole de Rouen. Ainsi, trois scénarios d’introduction du véhicule autonome : véhicules personnels, taxis et système de rabattement sont étudiés grâce à une analyse multicritères qualitative et participative. Cette mise en œuvre montre l’utilité de recourir aux principes méthodologiques de l’évaluation prospective et de l’évaluation durable pour formaliser l’évaluation du véhicule autonome. Plus généralement, prospective et durabilité apparaissent comme des notions clés pour une gestion réfléchie et distanciée de l’introduction du véhicule autonome.
... The autonomous-vehicles agent-based modeling studies are diverse. They include the travel and environmental impacts of autonomous vehicles , , the parking require-ments with the arrival of autonomous vehicles , , the traffic congestion caused by autonomous cars , the system performance of the self-driving vehicle , , , , , and the autonomous vehicles' modal share and travel modes , , . There are many critical variables that can impact the system performance of the AV, namely, fleet size, demand, strategy, ride-sharing, pricing schemes, configurations of stations, travel mode, vehicle capacity, service area, refuel/recharge time, maximum waiting time, and cruising time . ...
In recent years, autonomous vehicles (AVs), which observe the driving environment and lead a few or all of the driving duties, have garnered tremendous success. The field of AVs has been developing rapidly and has found many applications. As a safety requirement by policymakers, these vehicles must be evaluated before their deployment. The evaluation process for AVs is challenging because crashes are rare events, and AVs can escape passing predefined test scenarios. Therefore, capturing crashes and creating real test scenarios should be considered in order to have an evaluation approach that represents the real-world scenarios. One evaluation approach is based on the naturalistic field operational test (N-FOT), in which prototype AVs are driven by volunteers or test engineers on the roads. Unfortunately, this approach is time-consuming and costly because one needs to drive thousands of miles to experience a police-reported collision and nearly millions of miles for a fatal crash. Another approach is the Accelerated Evaluation method. The core idea of the Accelerated Evaluation approach is to modify the statistics of naturalistic driving so that safety-critical events are emphasized. This paper presents a brief survey of the advances that have occurred in the area of the evaluation of partly or fully AVs, starting with naturalistic field operational tests (N-FOTs). The review goes on to cover test matrix evaluation, worst-case scenario evaluation (WCSE), Monte Carlo simulations, and accelerated evaluation (AE). We also present all the simulation-based and agent-based modeling approaches that do not follow any evaluation protocol listed above. This study provides a scientific analysis of each of the evaluation techniques, focusing on their advantages/disadvantages, inherent restrictions, practicability, and optimality. The results reveal that the accelerated evaluation approach outperforms naturalistic field operational tests (N-FOTs), test matrix evaluation, worst-case scenario evaluation (WCSE), Monte Carlo simulations methods in some of the car-following, and lane-change studies when using specific models. Moreover, the agent-based model and augmented and virtual reality approaches show promising results in AVs evaluation. Furthermore, integrating machine and deep learning into the available AV evaluation methods can improve its performance and generate encouraging outcomes.
... Utilizing electric vehicles can further increase the potential benefits of ride-sharing, especially from the economic and environmental perspectives [39,40]. Their low energy consumption and emission could make them suitable for longer trips. ...
Shared mobility is a viable choice to improve the connectivity of lower-density neighbourhoods or suburbs that lack high-frequency public transportation services. In addition, its integration with new forms of powertrain and autonomous technologies can achieve more sustainable and efficient transportation. This study compares four shared-mobility technologies in suburban areas: the Internal Combustion Engine, Battery Electric, and two Autonomous Electric Vehicle scenarios, for various passenger capacities ranging from three to fifteen. The study aims to provide policymakers, transportation planners, and transit providers with insights into the potential costs and benefits as well as system configurations of shared mobility in a suburban context. A vehicle routing problem with time windows was applied using the J-Horizon software to optimize the costs of serving existing intra-community demand. The results indicate a similar fleet composition for Battery Electric and Autonomous Electric fleets. Furthermore, the resulting fleet for all four technologies is dominated by larger vehicle capacities. Due to the large share of driver cost in the total cost, the savings using a fleet of Autonomous Electric Vehicles are predicted to be 68% and 70%, respectively, compared to Internal Combustion and Battery Electric fleets.
... The application of electrification and automation in shared vehicle services at a reasonable extent can be seen in the near future. In Austin, the estimated mode share of shared autonomous electric vehicles (SAEV) was between 14% and 39% by assuming the SAEV service characteristics of $0.75 to $1.00 per mile fare, an 80 mi vehicle range, the availability of Level 2 charging infrastructure (provides a higher-output from a 240 V power source compared with the lower output provided by Level 1 charging infrastructure from a low 120 V power source), and an automation cost of $25,000 per vehicle (Chen and Kockelman 2016). The implementation of SAEV will increase the average transit trip length as shorter distance transit trips may be replaced by SAEV because of longer wait times and lower accessibility of transit services for shorter trips. ...
This chapter provides an overview of three major components of future emerging sustainable mobility modes and services, namely, shared vehicle services, shared bicycle services, and first mile/last mile solutions to reinforce public transportation services. To facilitate a comprehensive understanding of these components, their fundamental characteristics and functions, their associated engineering and planning factors or issues, and the impacts of automation technologies on the future of mobility services, as well as innovation strategies are the focus of the chapter.
... Their goal is to set up a syndicated revenue sharing system to increase passenger payments, gas costs and driver revenue. Other diferent pricing schemes proposed in literature and already implemented are analyzed by Ciari et al. , Perboli et al. , Chen et al. in ,  and , Li et al in , Jin et al. in  and Mourad et al.  in order to outline how one of a kind pricing techniques can bring about difering service call demands. ...
Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.
Autonomous vehicles (AVs) are expected to operate on Mobility-on-Demand (MoD) platforms because AV technology enables flexible self-relocation and system-optimal coordination. Unlike the existing studies, which focus on MoD with pure AV fleet or conventional vehicles (CVs) fleet, we aim to optimize the dynamic fleet management of an MoD system with a mixed autonomy of CVs and AVs. We consider a realistic case that human drivers may relocate freely and learn strategies to maximize their own compensation. In contrast, AVs are fully compliant with the platform's decisions. To achieve a high level of service provided by a mixed fleet, we propose that the platform prioritizes human drivers in the matching decisions, when on-demand requests arrive, and dynamically determines the optimal commission fee to influence drivers' behavior. However, it is challenging to make efficient real-time fleet management decisions when spatio-temporal uncertainty in demand and complex interactions among human drivers and operators are explicitly considered. To tackle the challenges, we develop a two-sided multi-agent deep reinforcement learning (DRL) approach, in which the operator acts as a supervisor agent on one side and makes centralized decisions on the mixed fleet, and each CV driver acts as an individual agent on the other side and learns to make desirable decisions non-cooperatively. For the first time, a scalable algorithm, which uses the actor-critic (A2C) method and mean-field approximation method to train the agents, is developed here for the mixed fleet management. Furthermore, deep neural networks (DNNs) are adapted to enhance the approximation for our high-dimensional and large-scale problems. We propose a two-head policy network to enable the supervisor agent to make two sets of decisions based on one policy network, which greatly reduces the computational time. The proposed approach is validated using a case study in New York City using real taxi trip data. Results show that our algorithm can make high-quality decisions quickly and outperform benchmark policies. Our fleet management strategy makes both the platform and the drivers better off, especially in scenarios with higher demand volume.
Automated Mobility on Demand (AMoD) is a concept that has recently generated much discussion. In cases where large-scale adoption of an automated taxi service is anticipated, the service’s impacts may become relevant to key transport system metrics, and thus to transport planners and policy-makers as well. In light of this increasingly important question, this paper presents an agent-based transport simulation with (single passenger) AMoD. In contrast to earlier studies, all scenario data (including demand patterns, cost assumptions and customer behaviour) is obtained for one specific area, the city of Zurich, Switzerland. The simulation study fuses information from a detailed bottom-up cost analysis of mobility services in Switzerland, a specifically tailored Stated-Preferences survey about automated mobility services conducted in the canton of Zurich, and a detailed agent-based transport simulation for the city, based on MATSim. Methodologically, a comprehensive approach is presented that iteratively runs these components to derive states in which service cost, waiting times and demand are in equilibrium for a cost-covering AMoD operator with predefined fleet size. For Zurich, several cases are examined, with 4,000 AMoD vehicles leading to the maximum demand of around 150,000 requests per day that can be attracted by the system. Within these parameters, the simulation results show that customers are willing to accept average waiting times of around 4 min at a price of 0.75 CHF/km. Further cost-covering cases with lower demand are presented, where either smaller fleet sizes lead to higher waiting times, or larger fleet sizes lead to higher costs. While our simulations indicate that an AMoD system in Zurich can bring benefits to the users, they show that the system impact is largely negative. Caused by modal shifts, our simulations show an increase of driven distance of up to 100%. All examined fleet configurations of the unregulated, cost-covering, single-passenger, door-to-door AMoD service are found to be highly counter-productive on a path towards a more shared and active transport system. Accordingly, policy recommendations for regulation are discussed.
Shared Autonomous Electric Vehicles (SAEVs) are expected to enter the transportation market in the upcoming decades. In this paper, we describe the preparation of a MATSim model for Vienna in which we add this new service as a new transportation mode. We simulate different pricing schemes for various SAEV fleet sizes and analyze their impacts. Our focus is on the impacts in regards of socioeconomic heterogeneity. One main finding of our paper is that the number of SAEV trips does not necessarily decrease for higher fares. It is instead the average travel time of SAEV rides which decreases if the service gets more expensive. Our simulation results for higher pricing schemes show that many people switch from bike or walk mode to SAEV. Public transport is also highly cannibalized by this new service regardless of the price, whereas SAEVs would always replace no more than 10% of car trips. SAEVs help reduce travel times significantly. People who do not have a car available in their household experience the greatest savings in travel time. A similar high share of SAEV trips is done by people older than 35 years. In regards of gender, our results reveal that women tend to use SAEVs for shorter trips.
Ridesourcing services from transportation network companies, like Uber and Lyft, serve the fastest growing share of U.S. passenger travel demand.1 Ridesourcing vehicles' high use intensity is economically attractive for electric vehicles, which typically have lower operating costs and higher capital costs than conventional vehicles. We optimize fleet composition (mix of conventional vehicles (CVs), hybrid electric vehicles (HEVs), and battery electric vehicles (BEVs)) and operations to satisfy demand at minimum cost and compare findings across a wide range of present-day and future scenarios for three cities. In nearly all cases, the optimal fleet includes a mix of technologies, HEVs and BEVs make up the majority of distance traveled, and CVs are used primarily for periods of peak demand (if at all). When life cycle air pollution and greenhouse gas emission externalities are internalized via a Pigovian tax, fleet electrification increases and externalities decrease, suggesting a role for policy. Externality reductions vary from 10% in New York (where externality costs for both gasoline and electricity consumption are relatively high and a Pigovian tax induces a partial shift to BEVs), to 22% in Los Angeles (where high gasoline and low electric grid externalities lead a Pigovian tax to induce a near-complete shift to BEVs).
Seit einigen Jahren lässt sich ein Wandel in der Automobil- und Mobilitätsbranche beobachten. Neue Mobilitätskonzepte entstehen durch veränderte politische, ökonomische, soziokulturelle und technologische Einflussfaktoren. Die erhöhte Nachfrage nach flexiblen und bedarfsgerechten Mobilitätslösungen verlangt nach neuen Konzepten für die Organisation geteilter Fahrten mit den automatisierten Fahrzeugen urbaner Mobilitätsdienste. Ridepooling beschreibt eines der neuen Mobilitätskonzepte und könnte geeignet sein, diese Bedarfe zu bedienen.
Ziel dieser Bachelorarbeit ist es, ein nutzerorientiertes, effizientes und nachhaltiges Dispositionssystem für einen automatisierten und auf die Zukunft ausgerichteten urbanen Ridepooling-Dienst zu konzeptionieren, prototypisch zu implementieren und im Nachhinein zu evaluieren.
Ein generisches und modulares Konzept auf Basis zuvor definierter Anforderungen wird konzipiert, um zu beantworten, wie das Dispositionssystem für die Erfüllung des Ziels und der heutigen Ansprüche gestaltet sein soll. Der konzipierte Entwurf zeigt auf, dass Module für die Auftrags- und Flottenverwaltung sowie Verarbeitung der Umweltzustände benötigt werden, um wesentliche Anforderungen zu erfüllen. Insbesondere eine dynamische Systemeinteilung in ein Datensammlungs- und Dienstzuweisungsintervall sowie eine effiziente Teilbarkeitsermittlung unter Berücksichtigung unterschiedlicher Umweltzustände und Kundenanforderungen von externen Systemen spielen eine wichtige Rolle.
Zudem wird durch eine prototypische Implementierung auf Basis eines Eclipse SUMO Simulationsszenarios aufgezeigt, dass das Konzept umsetzbar und lauffähig ist. Bei einer Bewertung wird im Vergleich zu einer anderen etablierten Mobilitätsform festgestellt, dass der Prototyp effizient und nachhaltig ist. Die Nutzerorientierung wird allerdings aufgrund einer minimal höheren Verzögerungsquote im Vergleich negativ bewertet. Zudem lässt sich die Nachhaltigkeit aufgrund eines auftretenden Fehlers nur positiv abschätzen und nicht eindeutig bewerten.
Electric vehicles (EVs) are more environmentally friendly than gasoline vehicles (GVs). To reduce environmental pollution caused by ride-hailing gasoline vehicles (RGVs), they have been gradually replaced with ride-hailing electric vehicles (REVs). Like RGVs, REVs can allow passengers to share trips with others. However, REVs are plagued by charging needs in daily operations. This study develops a simulation–optimization framework for the dynamic electric ride-hailing sharing problem. This problem integrates a dynamic electric ride-hailing matching problem (with sharing) and a dynamic REV charging problem, both of which aim to match REVs to passengers willing to share their trips with others and schedule the charging events of REVs on temporal and spatial scales, respectively. The dynamic electric ride-hailing matching problem is divided into a set of electric ride-hailing matching subproblems by a rolling horizon approach without a look-ahead period, while the dynamic REV charging problem is divided into a set of REV charging subproblems by a rolling horizon approach with look-ahead periods. Each REV charging subproblem incorporates a novel charging strategy to determine the charging schedules of REVs and relieve the charging anxiety by considering the information of requests, REVs, and charging stations. Each REV charging subproblem is formulated as a mixed integer linear program (MILP), whereas each electric ride-hailing matching subproblem is formulated as a mixed integer nonlinear program (MINLP). The MINLP and MILP are solved by the artificial bee colony algorithm and CPLEX, respectively. The proposed simulation–optimization framework includes a simulation model which is used to mimic the operations of REVs and update and track the state of passengers and the charging processes at charging stations over time using the outputs of each MILP and MINLP. The results show that the proposed charging strategy outperforms the benchmarks with a shorter waiting time for charging and a higher matching percentage in the dynamic ride-hailing matching problem. The robustness of the proposed charging strategy is tested under different scenarios with changing the initial state of charge (SOC), the number of REVs, the number of charging piles at each charging station, the time to fully charge, and the distribution of charging piles. The results show that REV drivers can charge their vehicles more flexibly without waiting too long and then pick up more passengers under all test scenarios.
The goal of this study was to analyze the impact of private autonomous vehicles (PAVs), specifically their near-activity location travel patterns, on vehicle miles traveled (VMT). The study proposes an integrated mode choice and simulation-based parking assignment model, along with an iterative solution approach, to analyze the impacts of PAVs on VMT, mode choice, parking lot usage, and other system performance measures. The dynamic simulation-based parking assignment model determines the parking location choice of each traveler as a function of the spatial–temporal demand for parking from the mode choice model, whereas the multinomial logit mode choice model determines mode splits based on the costs and service quality of each travel mode coming, in part, from the parking assignment model. The paper presents a case study to illustrate the power of the modeling framework. The case study varies the percentage of persons with a private vehicle (PV) who own a PAV versus a private conventional vehicle (PCV). The results indicated that PAV owners traveled an extra 0.11 to 1.51 mi compared with PCV owners on average, and the PV mode share was significantly higher for PAV owners. Therefore, as PCVs are converted into PAVs in the future, the results indicate substantial increases in VMT near activity destinations. However, the results also indicated that adjusting parking fees and redistributing parking lot capacities could reduce VMT. The significant increase in VMT from PAVs implies that planners should develop policies to reduce PAV deadheading miles near activity locations, as the automated era comes closer.
With the growing awareness of environmental protection, it has emerged as inevitable for ride-sharing platforms to introduce electric vehicles (EVs) and incorporate into the trend of sustainable transportation. To promote the popularization of EVs in the ride-sharing economy, the platforms need to understand uncertainties faced by EV drivers and eco-friendly consumption behaviors exhibited by consumers. With an analytical model that captures the characteristics of ride-sharing participants, this paper examines the pricing mechanism of a profit-maximizing ride-sharing platform operating two ride services, a basic ride service via general vehicles powered by fossil fuel and a green ride service via EVs. We find the optimal price of green ride service under the dynamic pricing contract and the optimal wage under the dynamic wage contract. We show that if there are more consumers than drivers for the green ride service, the optimal solution is independent of the opportunity cost of EV drivers.
Ride-hailing systems with electric autonomous vehicles are recognized as a next-generation development to ease congestion, reduce costs and carbon emissions. In this paper, we consider the operation planning problem involving vehicle dispatching, relocation, and recharging decisions. We model the problem as a Markov Decision Process (MDP) to generate the optimal policy that maximizes the total profits. We propose a flexible policy to provide optimal actions according to the reward considering future requests and vehicle availability. We show that our model outperforms the predetermined rules on improving profits. To handle the curse-of-dimensionality caused by the large scale of state space and uncertainty, we develop an asynchronous learning method to solve the problem by approximating the value function. We first draw the samples of exogenous information and update the quality of approximations based on the quality of decisions, then approximate the exact cost-to-go value function by solving an approximation subproblem efficiently given the state at each period. Two variant algorithms are presented for cases with scarce and sufficient information. We also incorporate the state aggregation and post-decision analysis to further improve computational efficiency. We use a set of shared actual data from Didi platform to verify the proposed model in numerical experiments. To conclude, we extract managerial insights that suggest important guidelines for the ride-hailing operations planning problem.
The arrival of new technologies and innovations on mobility, such as automated vehicles, creates opportunities to tackle urban challenges. The evaluation of the impacts of these innovations on the mobility system requires a comprehensive set of criteria and parameters. This article proposes a method to measure the impacts of Shared Automated Electric Vehicles (SAEV) on mobility through a sustainability assessment. Based on an integrative literature study and on the context of AVENUE, a European project deploying automated shuttles in the public transport of European cities, a set of indicators is defined. These mobility indicators assess the social, environmental, economic, governance, and technical impacts of SAEV. The multiple dimensions of the mobility indicators contribute to filling gaps of knowledge about the performance of SAEV. The proposed method allows an evaluation and comparison of SAEV to other means of transport and thus strengthens scientifically based recommendations for transportation policies.
In this paper, we model and simulate special use cases of on-demand shared mobility services for the City of Ann Arbor, MI. We define shared mobility as any motor-vehicle-served transportation option between private vehicles and public transit, such as taxis, demand-responsive transit, and dynamic shuttles. Here, we present and evaluate a suite of four diﬀerent service types that could potentially complement existing transportation services in Ann Arbor. A novel aspect of this study is that it tests scenarios that were developed in consultation with city planners looking for insights into real-world problems. This study used fleet simulation software to test four service configuration scenarios for a hypothetical on-demand shared mobility service: citywide shuttle, a corridor-based downtown shuttle, a park and ride shuttle, and a transit-complementary service. Three levels of demand were tested for each scenario: 3%, 9%, and 15% of all private vehicle trips in the city. Findings indicated that citywide on-demand shared mobility services struggled to achieve higher vehicle occupancies than private vehicles at approximately 1.4. Service configurations with aggregated trip density resulted in slightly improved occupancies, as found in downtown- and park and ride shuttle scenarios. More impactful was aggregating demand by moving from “many-to-many” routing as with citywide floating services to “many-to-one” routing as with downtown- or park and ride shuttle services, which increased vehicle occupancy from 1.4 to almost 2. Lastly, we also discuss the potential beneﬁts of reduced congestion and parking needs.
The will to apply bio-inspired techniques to coordinate and control autonomous X vehicles (AXVs) has increased tremendously during the last decade due to their advantages in the face of complexity in today’s demanding applications. Thus, several bio-inspired approaches for multiple-entities optimization have been proposed in the literature for various limited applications, e.g., drone coordination, mobile robot formation maintenance. In all these strategies, the entities must plan their path and control their movements while coordinating their behavior w.r.t. the other members, and they must avoid collisions, so the task could be very difficult in the unstructured environments present in future manufacturing plants and goods transportation. Future applications of these bio-inspired techniques for coordination and control of AXVs include large warehouses, manufacturing, logistics, last-mile delivery, etc. The AXVs could be grouped to carry larger goods or they can act as swarm members when they do not have a common goal, but they must interact while they move to complete the allocated tasks and intersect their paths with the paths of other entities. As such, this paper illustrates the concept of applying such bio-inspired coordination and control techniques for the development of future manufacturing and goods transportation, a discussion being carried out regarding the advantages and disadvantages of several techniques for their use in specific applications.
Previous surveys of people’s attitudes toward automated vehicles (AVs) and transit integration have primarily taken place in large urban areas. AV-transit integration also has a great potential in small urban areas. This paper is based on a survey of people’s attitudes towards AV-transit integration carried out in two small urban areas in the US State of Wisconsin. A total of 266 finished responses were analyzed using text mining, factor analysis, and regression analysis. Results show that respondents know about AVs and driving assistance technologies and welcome AV-transit integration but are unsure about its potential impacts. Technology-savvy respondents were more positive but had more concerns about AV-transit integration than others. Respondents who enjoyed driving were not necessarily against transit, as they were more positive about AV-transit integration and were more willing to use automated buses than those who did not enjoy driving as much. Transit users were more positive toward AV-transit integration than non-transit users.
Shared autonomous vehicles (SAVs) have been widely studied in the recent literature. Agent-based simulations and theoretical models have extensively explored the effects on travel service, fleet size, and congestion using heuristic dispatching strategies to match SAVs with on-demand passengers. A major question that simulations have sought to address is the service rate or replacement rate: the number of passengers each SAV can serve. Thus far, the service rate has mostly been estimated through simulation. This paper investigates an analytical max-pressure dispatch policy, which aims to maximize passenger throughput under any stochastic demand pattern, which takes the form of a model predictive control algorithm. An analytical proof using Lyapunov drift techniques is presented to show that the dispatch policy achieves maximum stability. The service rate and minimum fleet sizes are derived analytically in this paper and can be achieved with the proposed dispatch policy. Simulation results show that the maximum stable demand is linearly related to the fleet size given. Also, it demonstrates how asymmetric demand necessitates rebalancing trips that affect service rates. Even though decreasing average waiting time is not the primary goal of this paper, stability ensures bounded waiting times, which is demonstrated in simulation.
This paper models autonomous ridesharing — multiple travelers simultaneously riding one shared autonomous vehicle (SAV) — in a network equilibrium setting with mixed SAV and human-driven vehicle (HV) traffic. We make two major methodological contributions. First, a novel one (SAV)-to-many (riders) matching is proposed to characterize the waiting times of an SAV and multiple travelers who share rides in the SAV during online matching, which is a nontrivial generalization of the one-to-one matching without ridesharing. Our matching characterization considers the possibilities of a traveler matched with an SAV starting from the same origin, whereto the SAV moved unoccupied as a result of either pickup or relocation, or with an in-service SAV that goes through the traveler's origin. Second, a section-based formulation for SAV ridesharing user equilibrium is introduced to characterize the SAV traveler flow, which accommodates the possibility that an SAV traveler's itinerary (OD pair) is different from that of the serving SAV and other travelers in the SAV. Unlike the existing link and route based ridesharing formulations, the notion of section both prevents undesired traveler en-route transfer(s) and allows travelers of multiple ODs to share rides, meanwhile respecting the SAV seat capacity constraint. In addition to the above two methodological contributions, the optimal SAV fleet size, fare, routing, and allocation (to in-service, pickup, and relocation states) decisions of a transportation network company (TNC) are formulated. The TNC decisions anticipate traveler reactions as characterized by a new multimodal autonomous ridesharing user equilibrium (MARUE), which is put forward with a proof of its existence and finds the endogenous market shares and road congestion effects of SAV/HV. Original insights are obtained from model implementation, including substantial systemwide benefit of ridesharing, marginal benefit of relocation in the presence of ridesharing, and diminishing economies of SAV size.
Ridesharing (RS) has been modeled as a specific multimodal equilibrium where solo and RS vehicles have different travel cost structures. An extended network structure that incorporates three types of traffic flows (i.e., solo drivers, RS drivers, and RS riders) has been useful in RS related network design problems and policy making. However, the extended network structure did not model the matching cost between drivers and riders at each node explicitly, leading to unreasonable or undesirable phenomena. For instance, RS drivers may drop off passengers at one node, proceed to the next link, and then pick up passengers again. This frequent pick-up and drop-off phenomenon needs to be fixed before any efficient policies can be proposed. This paper aims to develop a two-layer extended network that allows to model nodal cost whenever a pickup or a drop-off happens. Search friction of drivers and waiting time of riders will be formulated as well. Accordingly, bi-level network design problems are formulated for congestion tolling and platform pricing. For congestion tolling, we apply a differential toll scheme and analyze its impact on optimal tolls. For platform pricing, we propose two objectives: maximization of social welfare and maximization of the platform revenue, and compare their impact on system performances. Numerical examples are then performed for congestion tolling and platform pricing on the Braess network and the Sioux Falls network, respectively.
When attempts are made to incorporate shared autonomous vehicles (SAVs) into urban mobility services, public transportation (PT) systems are affected by the changes in mode share. In light of that, a simulation-based method is presented herein for analyzing the manner in which mode choices of local travelers change between PT and SAVs. The data used in this study were the modal split ratios measured based on trip generation in the major cities of South Korea. Subsequently, using the simulated results, a city-wide impact analysis method is proposed that can reflect the differences between the two mode types with different travel behaviors. As the supply–demand ratio of SAVs increased in type 1 cities, which rely heavily on PT, use of SAVs gradually increased, whereas use of PT and private vehicles decreased. Private vehicle numbers significantly reduced only when SAVs and PT systems were complementary. In type 2 cities, which rely relatively less on PT, use of SAVs gradually increased, and use of private vehicles decreased; however, no significant impact on PT was observed. Private vehicle numbers were observed to reduce when SAVs were operated, and the reduction was a minimum of thrice that in type 1 cities when SAVs and PT systems interacted. Our results can therefore aid in the development of strategies for future SAV–PT operations.
We envision a multimodal transportation system in the near future when autonomous vehicles (AVs) will be used to serve the first mile and last mile of transit trips. For this purpose, the current research proposes an optimization model for designing an integrated autonomous mobility-on-demand (AMoD) and urban transit system. The proposed model is a mixed-integer non-linear programming model that captures the strategic behavior of passengers in a multimodal network through a passenger assignment model. It determines which transit routes to operate, the frequency of the operating routes, the fleet size of AVs required in each transportation analysis zone to serve the demand and the passenger flow on both road and transit network. A Benders decomposition approach with several enhancements is proposed to solve the given optimization program. Computational experiments are presented for the Sioux Falls multimodal network. The results show a significant improvement in the congestion in the city center with the introduction and optimization of the integrated transportation system.
The rapidly aging society of Japan requires a quality transportation system that provides new mobility services combining innovative technologies to include all residents. The present study carries out a pilot test of the world’s first connected public transport system between autonomous buses (AB) and the light rail transit (LRT). In this system, telecommunication between two modes enabled the bus to fully self-drive and pull in precisely at an LRT stop. Reporting a result of the pilot test in Hiroshima, this paper analyzes (a) the feasibility of the system based on a monitor survey and (b) public acceptance based on a resident survey of local residents.
While researchers have stressed the potential of automated vehicle (AV) technology in improving mobility and accessibility for a range of people, only a few attempts have been made to examine the impact of this new technology on different segments of the population in a realistic setting using high-fidelity simulation. To fill this gap, we analyze the equity implications of Automated Mobility-on-Demand (AMoD) in three full-scale prototype cities using SimMobility, a state-of-the-art activity- and agent-based framework. The prototype cities were developed based on two auto-dependent typologies, representing cities largely in the US/Canada, and a dense transit-oriented typology. We perform equity analyses at the individual and income-group level, in order to reveal the winners and losers from the introduction of AVs under two scenarios: (1) AMoD Intro, in which a low-cost AMoD service competes with mass transit, and (2) AMoD Transit Integration, where AMoD complements mass transit, via access/egress connectivity service to rapid transit stations. We evaluate the following outcomes: induced demand by age and income groups, mode share by income levels, individual kilometers traveled by different modes and income levels, and the spatial distribution of change in fare and accessibility. Outcomes are considered as equity-oriented if they reduce accessibility gaps, particularly among disadvantaged populations. Our results indicate that in large population-dense and transit-oriented cities, the most equity-oriented outcomes can be achieved, due to extensive mass transit usage, which depresses car usage and restricts induced demand for AMoD. Such cities provide greater opportunities for low-income groups. Specifically, the AMoD Transit Integration scenario results in the best outcomes and implies a new market share, as disadvantaged groups, such as children and low-income individuals, were able to travel more using the integrated AMoD-transit service. Nevertheless, in car-dependent cities, where accessibility gaps are much larger, AMoD Intro scenario performs better compared to AMoD Transit Integration, as it serves the less accessible population and significantly improves their opportunities.
Automated vehicles (AVs) have great potential to revolutionize the transportation sector and landscapes of future cities. The impacts of AVs on urban space, however, are far from clear. Mobility-on-Demand (MOD) services, on the other hand, are readily available in many places. This study seeks to explore (1) how Automated Mobility-on-Demand (AMOD) might affect urban residents' levels of accessibility and their residential relocation decisions; and (2) how these impacts might vary across space and socioeconomic groups. We use an agent-based microsimulation platform to assess two future AMOD scenarios in Singapore relative to a baseline. Results suggest that the addition of AMOD could enhance the overall accessibility of the population, but not if private transport modes, including private cars, taxis, and human-driven on-demand services, are prohibited. On the other hand, if private modes are eliminated, AMOD could alleviate inequality in accessibility as it appears to benefit the disadvantaged socioeconomic groups to a larger extent. We also find that AMOD deployment would not induce outward migration, nor would it increase home-work location imbalance. This study demonstrates how large-scale microsimulation can be leveraged to assess AMOD scenarios. The findings have some implications for preparing for the inevitable and potentially disruptive emergence of AVs.
Shared autonomous vehicles (SAVs) will likely emerge in many urban settings over the coming decade and may significantly impact passenger travel. SAV fleet managers, the public, and policymakers may be attracted to all-electric drivetrains' lower operating costs and environmental benefits, but fleet managers will need to account for charging times and range limitations of EV battery packs. This study investigates a variety of potential electric SAV (SAEV) fleet designs and charging strategies that relate to vehicle range decisions, battery state-of-charge buffers, charging station capacity choices, response times, and the ability of currently-charging vehicles to accept new trips. The agent-based transportation tool POLARIS is used to simulate over 36 SAEV management scenarios serving passenger travel across Illinois' Bloomington region, and a subset of the same for the Greater Chicago region. Results show a mixed fleet of short (100-mi) and long (250-mi) range SAEVs performs better than a homogenous short-range fleet, with lower empty vehicle miles traveled (eVMT), higher average vehicle occupancies, and lower idling time. Charging and service priority policies are both required, but at different times of the day to accommodate slow Level 2 chargers, but is not as important for DCFC charging stations. SAEVs can stay in place longer (1 hr versus 15 min) to keep eVMT low, but only if long-range SAEVs are in the fleet and the region is small. SAEVs in large regions are exposed to location-specific trip requests when idling in place, and need to have high average state of charge (SoC) across the fleet to serve all incoming requests. Homogenous fleets need careful prioritizing of charging over service for an acceptable multi-day operation when using a largely Level 2 charging station 2 network. Smart siting of EVCS and availability of fast chargers remain key to minimizing fleet size and keeping response times low.
Automated vehicles (AVs) may enter the consumer market with various stages of automation in 10 years or even sooner. Meanwhile, regional planning agencies are envisioning plans for time horizons out to 2040 and beyond. To help decision makers understand the effect of AV technology on regional plans, modeling tools should anticipate its impact on transportation networks and traveler choices. This research uses the Seattle, Washington, region's activity-based travel model to test a range of travel behavior impacts from AV technology development. The existing model was not originally designed with AVs in mind, so some modifications to the model assumptions are described in areas of roadway capacity, user values of time, and parking costs. Larger structural model changes were not yet considered. Results of four scenario tests show that improvements in roadway capacity and in the quality of the driving trip may lead to large increases in vehicle miles traveled, while a shift to per mile usage charges may counteract that trend. Travel models will need to have major improvements in the coming years, especially with regard to shared ride, taxi modes, and the effect of multitasking opportunities, to better anticipate the arrival of this technology.
While the State of New Jersey has an extensive commuter railroad, light rail and express and local bus systems, these transit systems serve only 5% of the State's daily work trips and a substantially smaller percentage of the daily non-walking trips. While extensive, these systems are incapable of offering competitive service to the diverse bulk of everyday trips that are spatially and temporally distributed throughout the state. Consequently, these trips are served by the ubiquity and efficient connectivity afforded by the state's roadway system through the use of on-demand personal automobiles. This paper describes attempts to conceptually design a statewide transit system that would afford automobile-like service ubiquitously to essentially all daily non-walking trips. Technologically, the transit system is an area-wide Automated Transit Network (ATN) of auto-sized vehicles offering personalized on-demand non-stop service between all stations, commonly known as a Personal Rapid Transit (PRT) system. Presented is one state-wide design consisting of 8,099 stations interconnected by 16,926 kilometers of one-way guideway in which 215,000 vehicles serve 25 million daily trips.
Ninety-five percent of automobile commuters in the United States park free at work. To deal with the traffic congestion and air pollution caused by parking subsidies, California law now requires many employers to offer employees the option to cash out their parking subsidies. Similar Federal legislation has been proposed. This nationwide survey found that employers in the United States off employees 84.8 million free parking spaces. Employers own 65.3 million of these free parking spaces, and rent the other 19.5 million. Employers of fewer than twenty employees provide more than half of all employer-paid parking spaces.
Autonomous vehicles (AVs) may significantly change traveler behavior and network congestion. Empty repositioning trips allow travelers to avoid parking fees or share the vehicle with other household members. Computer precision and reaction times may also increase road and intersection capacities. AVs are currently being test driven on public roads and may be publicly available within the next two decades; they therefore may be within the span of 20- to 30-year planning analyses. Despite this time scale, AV behavior has yet to be incorporated into planning models. This paper presents a multiclass, four-step model that includes AV repositioning to avoid parking fees (although incurring additional fuel costs) and increases in link capacity as a function of the proportion of AVs on the link. Demand is divided into classes by value of time and AV ownership. Mode choice-parking, repositioning, or transit-is determined through a nested logit model. Traffic assignment is based on a generalized cost function of time, fuel, and tolls. The results on a city network show that transit ridership decreases and the number of personal vehicle trips sharply increases as a result of repositioning. However, increases in link capacity offset the additional congestion. Although link volume increases significantly, only modest decreases in average link speeds are observed.
The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of personal transportation in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.
Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.
In this paper, we present an optimization approach to depot location in one-way carsharing systems where vehicle stock imbalance issues are addressed under three trip selection schemes. The approach is based on mixed-integer programming models whose objective is to maximize the profits of a carsharing organization considering all the revenues and costs involved. The practical usefulness of the approach is illustrated with a case study involving the municipality of Lisbon, Portugal. The results we have obtained from this study provided a clear insight into the impact of depot location and trip selection schemes on the profitability of such systems.