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The morning commute problem with endogenous shared autonomous vehicle penetration and parking space constraint
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Morning commuting trips commonly involve a number of transportation modes and departure times. This study extends the standard bottleneck model by considering both regular and shared autonomous vehicles with and without parking space constraint, taking into account the travel time-dependent fuel cost that is associated with each vehicle. In this study the parking space constraint has no effect on shared autonomous vehicles and only commuters who share autonomous vehicles exhibit ridesharing behavior, differing from those utilizing regular vehicles. The dynamic departure patterns and endogenous penetration rates associated with shared autonomous vehicles are determined with respect to parking capacity. Analytical results not only provide several important propositions, but also include the optimal solutions for parking capacity and ridesharing occupancy from the perspective of the system optimum in terms of various indicators, under which parking capacity is a binding constraint, and the first shared autonomous vehicle arrives at the bottleneck as the last regular vehicle leaves. This work is expected not only to motivate related research, but also to promote the development of new strategies and methods for allocating a limited number of parking spaces and regulating the travel behavior of commuters in urban areas.
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... Due to these advantages, AV technology has experienced explosive growth, and substantial recent literature focuses on the potential changes after introducing FAVs and AVs. These works include changes in travel behavior [14][15][16][17][18][19], and some of the works focus on changes in work-home location [20,21]. Some works focus on travel time savings [22][23][24], while the other strands of works look at the time-saving effects and merits of using AVs with public transportation choices [25][26][27]. ...
This study investigates the impact of environmental concerns, concerns about potential accidents, and the perceived advantages of Fully Autonomous Vehicles on individuals' willingness to buy and perceived value of these vehicles. Our research, conducted through a comprehensive survey with over 180,000 respondents in Japan and analyzed using structural equation modeling, reveals a nuanced disparity between willingness to buy and perceived value. We find that individuals concerned with natural environment conservation are more likely to purchase Fully Autonomous Vehicles due to their broader interest in societal issues and belief in the potential of new technologies like Fully Autonomous Vehicles as solutions. However, these individuals attribute a lower perceived value to these vehicles, mainly because their adoption does not directly contribute to natural environment conservation. Additionally, our results show that those recognizing the potential advantages of Fully Autonomous Vehicle technology have a higher willingness to buy and perceived value, while those with apprehensions about the technology are less likely to purchase and attribute a lower perceived value to these vehicles. This study offers vital insights for policy and planning, highlighting the complex interplay of factors influencing the willingness to buy and perceived value of Fully Autonomous Vehicles, critical for strategizing their adoption.
... Situational awareness acts as an input for the decision-making system, facilitating both short-term and long-term planning. Short-term planning entails tasks such as generating paths, evading obstacles, and managing incidents and maneuvers, while long-term planning encompasses mission and route planning [12]. Inaccuracies in decision-making primarily arise from factors linked to the system or human involvement. ...
Autonomous electric vehicles (AEVs) hold great promise for the future of automotive engineering, but safety remains a significant challenge in their development and commercialization. Therefore, conducting a comprehensive analysis of AEV development and reported accidents is crucial. This paper reviews the levels of automation in AEVs, their disengagement frequencies, and on-road accident reports. According to the report, numerous manufacturers thoroughly tested AEVs across a distance of more than 3.9 million miles between 2014 and 2022. Disengagement frequencies vary among manufacturers, and approximately 65% of accidents during this period occurred while AEVs were operating in autonomous mode. Notably, the majority of accidents (90%) were caused by other road users, with only a small fraction (∼8%) directly attributed to AEVs. Enhancing AEVs' ability to detect and mitigate safety risks from external sources has the potential to significantly improve their safety. This paper provides valuable insights into AEV safety by emphasizing the importance of comprehensively understanding AEV development and reported accidents. Through the analysis of disengagement and accident reports, the study highlights the prevalence of passive accidents caused by other road users. Future research should concentrate on enabling AEVs to effectively detect and respond to safety risks originating from external sources to enhance AEV safety. Overall, this analysis contributes to the ongoing efforts in AEV development and provides guidance for strategies aimed at improving their safety features.
... Situational awareness acts as an input for the decision-making system, facilitating both short-term and long-term planning. Short-term planning entails tasks such as generating paths, evading obstacles, and managing incidents and maneuvers, while long-term planning encompasses mission and route planning [12]. Inaccuracies in decision-making primarily arise from factors linked to the system or human involvement. ...
Autonomous electric vehicles (AEVs) hold great promise for the future of automotive engineering, but safety remains a significant challenge in their development and commercialization. Therefore, conducting a comprehensive analysis of AEV development and reported accidents is crucial. This paper reviews the levels of automation in AEVs, their disengagement frequencies, and on-road accident reports. According to the report, numerous manufacturers thoroughly tested AEVs across a distance of more than 3.9 million miles between 2014 and 2022. Disengagement frequencies vary among manufacturers, and approximately 65% of accidents during this period occurred while AEVs were operating in autonomous mode. Notably, the majority of accidents (90%) were caused by other road users, with only a small fraction (approximately 8%) directly attributed to AEVs. Enhancing AEVs' ability to detect and mitigate safety risks from external sources has the potential to significantly improve their safety. This paper provides valuable insights into AEV safety by emphasizing the importance of comprehensively understanding AEV development and reported accidents. Through the analysis of disengagement and accident reports, the study highlights the prevalence of passive accidents caused by other road users. Future research should concentrate on enabling AEVs to effectively detect and respond to safety risks originating from external sources to enhance AEV safety. Overall, this analysis contributes to the ongoing efforts in AEV development and provides guidance for strategies aimed at improving their safety features.
... Situational awareness acts as an input for the decision-making system, facilitating both short-term and long-term planning. Short-term planning entails tasks such as generating paths, evading obstacles, and managing incidents and maneuvers, while long-term planning encompasses mission and route planning [12]. Inaccuracies in decision-making primarily arise from factors linked to the system or human involvement. ...
Autonomous electric vehicles (AEVs) hold great promise for the future of automotive engineering, but safety remains a significant challenge in their development and commercialization. Therefore, conducting a comprehensive analysis of AEV development and reported accidents is crucial. This paper reviews the levels of automation in AEVs, their disengagement frequencies, and on-road accident reports. According to the report, numerous manufacturers thoroughly tested AEVs across a distance of more than 3.9 million miles between 2014 and 2022. Disengagement frequencies vary among manufacturers, and approximately 65% of accidents during this period occurred while AEVs were operating in autonomous mode. Notably, the majority of accidents (90%) were caused by other road users, with only a small fraction (∼8%) directly attributed to AEVs. Enhancing AEVs' ability to detect and mitigate safety risks from external sources has the potential to significantly improve their safety. This paper provides valuable insights into AEV safety by emphasizing the importance of comprehensively understanding AEV development and reported accidents. Through the analysis of disengagement and accident reports, the study highlights the prevalence of passive accidents caused by other road users. Future research should concentrate on enabling AEVs to effectively detect and respond to safety risks originating from external sources to enhance AEV safety. Overall, this analysis contributes to the ongoing efforts in AEV development and provides guidance for strategies aimed at improving their safety features.
Shared transport is an emerging transportation system where travelers share a vehicle either simultaneously as a group (e.g. ride-sharing, ride-sourcing, or customized bus) and share the cost of the journey in the process, or over time (e.g. car-sharing or bike-sharing) as personal rental. We review shared transport research articles published in major operations research, management science and transportation journals from 2010 to 2021. In reviewing these studies, our objective is to assess and present the operations management research of shared transport from a macro level such that new researchers will be attracted and established researchers will be motivated to contribute to this emerging field. We divide the operations management research of shared transport into eight attributes (shared transport mode, decision level, characteristics of demand, decision function, decision objective, modeling approach, solution method, and type of data). Cross tabulations are done among these eight attributes to gain more profound insights. Furthermore, we divide the decision function attribute into 36 subcategories. Maturity and recent attention are calculated as indicators for evaluating the research potential in the decision function attribute. After analyzing the research status, we look forward to the prospect and provide relevant recommendations for future research.
This paper examines the household commuting problem in a bi-modal transportation that includes both autos and school buses. Traditional studies of household commuting have assumed that adults first drop off their children at school before driving to work. However, with the increasing use of school buses in metropolitan areas, household commuting patterns have changed. This paper investigates the impact of school buses on household travel behavior during morning peak hours. Simultaneously, this paper also takes into account the positive utilities of household commuters’ activities at home, school and workplace, as well as the negative utilities of travel time and schedule delay. In this paper, based on activity approach, first, a net utility function is developed using these factors. Then, based on the net utility function, the occurrence conditions of all the possible equilibrium travel patterns are analytically solved and the properties of the user equilibrium are researched. Specifically, this paper examines how equilibrium travel patterns are affected by school bus fares and school-work start time difference, and at equilibrium travel patterns, the net utilities of household commuters are also analyzed. Finally, a first-best time-varying toll model is suggested to alleviate traffic congestion caused by household commuting.
Emerging autonomous vehicles (AVs) are expected to bring about a revolution in both the automotive industry and transportation systems. Introducing AVs into the existing mobility system with human-driven vehicles (HVs) yields mixed traffic with the following new features: in-vehicle compensation on value of time for AV users, distinct road capacities for pure AV and HV flows, and stochastic road capacity for the inseparable AV-HV traffic pattern. In this paper, we aim to investigate equilibrium traffic dynamics for the morning commuting problem where AVs and HVs coexist in a transportation corridor by considering these new features, and also explore several novel mixed AV-HV traffic management strategies. The AV-HV traffic pattern could be either separable (i.e., pure AV flow and pure HV flow depart from home in different periods) or inseparable, depending on the user profile condition. In addition to deriving departure time equilibriums for scenarios with separable traffic flows, significant effort is put into the scenario with an inseparable AV-HV traffic pattern, where stochastic road capacity is taken into account. Based on these equilibrium traffic analyses, we propose and explore some new traffic management strategies, including AV certificate of entitlement management scheme for scenarios with separable traffic flows and departure-period management (DPM) scheme and lane management policies for the scenario with an inseparable AV-HV traffic pattern. Eligibilities for applying these strategies are analytically derived and extensively discussed, and numerical experiments are conducted to demonstrate our theoretical findings and reveal the underlying impacts of road capacity randomness. Some lessons learned from the numerical experiments are (i) overlooking the impact of road capacity uncertainty will lead to an overestimation of system performance and even yield biased policymaking, (ii) the full dedicated-lane policy is the preferred option for the medium-level AV situation and partial dedicated-lane policies are more attractive choices for the early AV era or a market with a high AV share, and (iii) the DPM scheme could be a better substitute for partially dedicated-lane policies.
Funding: This study was supported by the Ministry of Education of Singapore [Project T2EP40222-0002 under the MOE Tier 2 Grant] and the National Natural Science Foundation Council of China [Grant 72001133 and the Excellent Young Scientists Fund Program (Overseas)].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0469 .
Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.
Abstract This paper examines the dynamic user equilibrium of the morning commute problem in the presence of ridesharing program. Commuters simultaneously choose departure time from home and commute mode among three roles: solo driver, ridesharing driver, and ridesharing rider. Considering the congestion evolution over time, we propose a time-varying compensation scheme to maintain a positive ridesharing ridership at user equilibrium. To match the demand and the supply of ridesharing service over time, the compensation scheme should be set according to the inconvenience cost functions and the out-of-pocket cost functions. When the price charged per time unit is higher than the inconvenience cost per time unit perceived by the ridesharing drivers, the ridesharing participants will travel at the center of peak hours and solo drivers will commute at the two tails. Within the feasible region with positive ridership, the ridesharing program can reduce the congestion and all the commuters will be better off. To support system optimum (SO), we derive a time-varying toll combined with a flat ridesharing price from eliminating queuing delay. Under SO toll, the ridesharing program can attract more participants and have an enlarged feasible region. This reveals that the commuters are more tolerant to the inconvenience caused by sharing a ride at SO because of the lower travel time. Compared with no-toll equilibrium, both overall congestion and individual travel cost are further reduced at SO.
Autonomous vehicles can profoundly change parking behavior in the future. Instead of searching for parking, autonomous vehicle owners get dropped off at their final destination and send their occupant-free cars to a parking spot. In this paper, we study the impact of parking in a bi-class morning commute problem with autonomous and regular vehicles. We consider a spatial distribution of parking spaces, which allows us to capture the parking location of travelers. In the equilibrium condition, autonomous vehicles leave home later (than regular vehicles), and park farther away from their destination. The regular vehicle travelers, however, leave home sooner to park closer to the destination with a smaller walking distance. The reverse occurs if regular travelers abdicate walking and take a faster mode from their parking space to the city center. To optimize the system, we develop temporal and spatial parking pricing strategies and a new parking supply design scheme, as practical alternatives for the conventional dynamic congestion pricing. The proposed parking pricing strategies incentivize commuters to adjust their travel schedules by charging a parking price that increases with distance from the city center. Hence, those who park closer to their destination have to pay less. This is a counter-intuitive finding at first which arises from a trade-off with the earliness penalty of users who park close to the destination. We show that system optimality is also reached by redistributing the parking supply. Our numerical experiments capture the impact the autonomous vehicle penetration rate on the performance of the system and the proposed parking pricing and design strategies
The rapid development of smartphone technology has led to the increased popularity of dynamic ridesharing apps used to organize ad hoc ridesharing trips between strangers on short notice. To support such real-time on-demand services, cost-sharing between drivers and riders is commonly centrally determined by ridesharing apps according to prescribed rules. To highlight the impacts of appropriate cost-sharing strategies on the success of ridesharing programs, this paper models the mode choices of a group of heterogeneous travelers with continuously distributed values of time in a single-corridor network, considering the complex interactions between travelers' mode choices and the attractiveness of ridesharing in terms of rider/driver waiting/detouring times and matching probabilities. The equilibrium state under any given cost-sharing strategy is described by a system of variational inequalities based on which the existence of equilibria is established. With the proposed modeling framework, various cost-sharing strategies are examined to avoid mode shifts among transit users to autos and/or reduce vehicular traffic in the short run; the necessary conditions for cost-sharing strategies to sustain participation and/or reduce vehicle usage are explicitly provided. It is shown that when driving alone is faster but more expensive than public transit, no cost-sharing strategy exists to sustain an active ridesharing platform without inducing transit users to join the ridesharing program. Moreover, the existence of cost-sharing strategies capable of reducing vehicular traffic on the road is not always guaranteed, depending on the costs of driving alone and taking public transit in the considered corridor, fuel prices, and travelers' prioritization of safety and privacy. Furthermore, it is found that the initial state with no ridesharing participants is an equilibrium under any cost-sharing strategy if the additional cost incurred by a traveler through participating in a ridesharing program is nonnegative. This explains the difficulty of initiating a ridesharing program and implies the initial necessity of subsidizing all intended riders and/or drivers to encourage participation.
This study is the first in the literature to model the joint equilibrium of departure time and parking location choices when commuters travel with autonomous vehicles (AVs). With AVs, walking from parking spaces to the work location is not needed. Instead, AVs will drop off the commuters at the workplace and then drive themselves to the parking spaces. In this context, the equilibrium departure/arrival profile is different from the literature with non-autonomous vehicles (non-AVs). Besides modeling the commuting equilibrium, this study further develops the first-best time-dependent congestion tolling scheme to achieve the system optimum. Also, a location-dependent parking pricing scheme is developed to replace the tolling scheme. Furthermore, this study discusses the optimal parking supply to minimize the total system cost (including both the travel cost and the social cost of parking supply) under either user equilibrium or system optimum traffic flow pattern. It is found that the optimal planning of parking can be different from the non-AV situation, since the vehicles can drive themselves to parking spaces that are further away from the city center and walking of commuters is avoided. This paper sheds light on future parking supply planning and traffic management.
Dynamic ride-sharing systems enable people to share rides and increase the efficiency of urban transportation by connecting riders and drivers on short notice. Automated systems that establish ride-share matches with minimal input from participants provide convenience and the most potential for system-wide performance improvement, such as reduction in total vehicle-miles traveled. Indeed, such systems may be designed to match riders and drivers to maximize system performance improvement. However, system-optimal matches may not provide the maximum benefit to each individual participant. In this paper, we consider a notion of stability for ride-share matches and present several mathematical programming methods to establish stable or nearly stable matches, where we note that ride-share matching optimization is performed over time with incomplete information. Our numerical experiments using travel demand data for the metropolitan Atlanta region show that we can significantly increase the stability of ride-share matching solutions at the cost of only a small degradation in system-wide performance.
This paper studies the traffic flow patterns in a single bottleneck corridor with a dynamic ridesharing mode and dynamic parking charges. Schemes with different ridesharing payments and shared parking prices are investigated. Besides the scheme with constant parking charges and ridesharing payments, dynamic parking charges and ridesharing payments are derived to achieve congestion-free traffic in the corridor. With the dynamic ridesharing ratios, it is found that genuinely nonlinear departure rates and travel time functions can be generated in certain ridesharing cases, which was not observed in the traditional ADL model (Arnott et al., 1990) for the morning commute problems without ridesharing or with constant ridesharing ratios. Moreover, comparing different configurations of ridesharing arrangements and parking charges, the results show that constant parking charges with constant ridesharing payments may not significantly improve system performance over the traditional morning commute with solo-drivers, while dynamic parking charges with properly selected constant ridesharing payments can achieve better system performance in terms of vehicle-miles-traveled, vehicle-hours-traveled and total travel costs, by encouraging ridesharing and spreading vehicular demand over time to eliminate queuing delays.
We study the shared autonomous vehicle (SAV) routing problem while considering congestion. SAVs essentially provide a dial-a-ride service to travelers, but the large number of vehicles involved (tens of thousands of SAVs to replace personal vehicles) results in SAV routing causing significant congestion. We combine the dial-a-ride service constraints with the linear program for system optimal dynamic traffic assignment, resulting in a congestion-aware formulation of the SAV routing problem. Traffic flow is modeled through the link transmission model, an approximate solution to the kinematic wave theory of traffic flow. SAVs interact with travelers at origins and destinations. Due to the large number of vehicles involved, we use a continuous approximation of flow to formulate a linear program. Optimal solutions demonstrate that peak hour demand is likely to have greater waiting and in-vehicle travel times than off-peak demand due to congestion. SAV travel times were only slightly greater than system optimal personal vehicle route choice. In addition, solutions can determine the optimal fleet size to minimize congestion or maximize service.
This study investigates the morning commute problem with both household and individual travels, where the household travel is a shared ride of household (family) members. In particular, it considers the situation when a proportion of commuters have to drive their children to school first and then go to work (household travel). For household travel, departure time choice is a joint decision based on all household members’ preferences. Unlike the standard bottleneck model, the rush-hour dynamic traffic pattern with mixed travelers (household travelers and individual travelers) varies with the numbers of individual travelers and households, as well as the schedule difference between school and work. Given the numbers of individual travelers and households, we show that by appropriately coordinating the schedules of work and school, the traffic congestion at the highway bottleneck can be relieved, and hence the total travel cost can be reduced. This is because, departure/arrival of individual and household travels can be separated by schedule coordination. System performance under schedule coordination is quantified in terms of the relative proportions of the two classes of travelers and is compared with the extreme case when the same desired arrival time applies to both schooling and working. Furthermore, the efficiency of work and school schedule coordination in reducing travel cost is bounded. This efficiency is also compared with that at the system optimum where queuing is fully eliminated and schedule delay cost is minimized (achieved by a joint scheme of first-best pricing and schedule coordination).
‘Autonomous cars’ are cars that can drive themselves without human control. Autonomous cars can safely drive closer together than cars driven by humans, thereby possibly increasing road capacity. By allowing drivers to perform other activities in the vehicle, they may reduce the value of travel time losses (VOT). We investigate the effects of autonomous cars using a dynamic equilibrium model of congestion that captures three main elements: the resulting increase in capacity, the decrease in the VOT for those who acquire one and the implications of the resulting changes in the heterogeneity of VOTs. We do so for three market organizations: private monopoly, perfect competition and public supply. Even though an increased share of autonomous cars raises average capacity, it may hurt existing autonomous car users as those who switch to an autonomous car will impose increased congestion externalities due to their altered departure time behaviour. Depending on which effect dominates, switching to an autonomous vehicle may impose a net negative or positive externality. Often public supply leads to 100% autonomous cars, but it may be optimal to have a mix of car types, especially when there is a net negative externality. With a positive (negative) externality, perfect competition leads to an undersupply (oversupply) of autonomous cars, and a public supplier needs to subsidise (tax) autonomous cars to maximise welfare. A monopolist supplier ignores the capacity effect and adds a mark-up to its price.