Funding: Singapore-MIT Alliance for Research and Technology (SMART), 2016-2020
As part of the Future Urban Mobility (FM) IRG of the Singapore-MIT Alliance for Research and Technology (SMART), the team led by Prof. Jinhua Zhao combines behavioral science and transportation technology to envision a future urban mobility system for Singapore that integrates public transit, walking and bicycling, shared mobility, and autonomous vehicles. The current phase of the project consists of four topics: 1. Examining the formation process of people’s preferences for autonomous vehicles (AVs); 2. Monitoring emotional and physiological responses during AV rides; 3. Designing the integration of on-demand AV service with public transport systems; and 4. Social mobility sharing in the interest of joint optimization of network efficiency and preference for human interaction.
Topic #1 Preference Formation for Autonomous Vehicles: Stated Preference Surveys Before and After Actual Trial Rides. The project aims to study the formation process of people’s preferences for autonomous vehicles (AVs). In the short term, we will implement multiple stages of stated preference surveys before and after trial rides in AV prototypes to examine how people learn and adapt to new transportation technology in the context of last-mile modal choices.
Topic #2 Electroencephalograph (EEG) and Physiological Measures of Emotional Responses to Autonomous Vehicle using EEG Neuroheadset. The project aims to measure and analyze people’s emotional responses when riding autonomous vehicles in various traffic conditions. We will use an electroencephalograph (EEG) neuro-headset as the main measurement and other physiological measures to corroborate. Link: Emotional Travel
Topic #3 Integrating Autonomous Vehicles with Public Transit Service: Last Mile Service to MRT Stations. The project aims to design and test the new mobility scenarios in which autonomous vehicles are embedded into the public transit system. We will simulate the on-demand last-mile service to and from Singapore MRT stations, testing a variety of business, operation, pricing, and regulation models with different degrees of mixture between autonomous vehicles and public transit services. Link: Governing Autonomous Vehicles
Topic #4 Social Mobility Sharing: Joint optimization of Network Efficiency and Preference for Human Interaction. This project examines dynamic mobility sharing from the perspective of social interaction and develops human-centric ride-sharing systems that respect both network efficiency and individuals’ preferences (or lack thereof) for human interaction. Link: Social Mobility Sharing
-
Integrating Shared Autonomous Vehicle in Public Transportation System: A Supply-Side Simulation of the First-Mile Service in Singapore
Transportation Research Part A(2018)This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently.
-
Mobility Sharing as a Preference Matching Problem
IEEE Transactions on Intelligent Transportation Systems(2018)Traffic congestion, dominated by single-occupancy vehicles, reflects not only transportation system inefficiency and negative externalities, but also a sociological state of human isolation. Advances in information and communication technology are enabling the growth of real-time ridesharing to improve system efficiency. While ridesharing algorithms optimize passenger matching based on efficiency criteria (maximum number of paired trips, minimum total vehicle-time or vehicle-distance traveled), they do not explicitly consider passengers' preference for each other as the matching objective. We propose a preference-based passenger matching model, formulating ridesharing as a maximum stable matching problem. We illustrate the model by pairing 301,430 taxi trips in Manhattan in two scenarios: one considering 1,000 randomly generated preference orders, and the other considering five sets of group-based preference orders. In both scenarios, compared with efficiency-based matching models, preference-based matching improves the average ranking of paired fellow passenger to the near-top position of people's preference orders with only a small efficiency loss at the individual level, and a moderate loss at the aggregate level. The near-top-ranking results fall in a narrow range even with the random variance of passenger preference as inputs.
Cite as: Zhang, Hongmou, and Jinhua Zhao. 2018. “Mobility Sharing as a Preference Matching Problem.” IEEE Transactions on Intelligent Transportation Systems.
-
How does Ridesourcing Substitute for Public Transit? A geospatial perspective in Chengdu, China
Journal of Transport Geography(2020)The explosive growth of ridesourcing services has stimulated a debate on whether they represent a net substitute for or a complement to public transit. Among the empirical evidence that supports discussion of the net effect at the city level, analysis at the disaggregated level from a geospatial perspective is lacking. It remains unexplored the spatiotemporal pattern of ridesourcing’s effect on public transit, and the factors that impact the effect. Using DiDi Chuxing data in Chengdu, China, this paper develops a three-level structure to recognize the potential substitution or complementary effects of ridesourcing on public transit. Furthermore, this paper investigates the effects through exploratory spatiotemporal data analysis and examines the factors influencing the degree of substitution via linear, spatial autoregressive, and zero-inflated beta regression models. The results show that 33.1% of DiDi trips have the potential to substitute for public transit. The substitution rate is higher during the day (8:00–18:00), and the trend follows changes in public transit coverage. The substitution effect is more exhibited in the city center and the areas covered by the subway, while the complementary effect is more exhibited in suburban areas as public transit has poor coverage. Further examination of the factors impacting the relationship indicates that housing price is positively associated with the substitution rate, and distance to the nearest subway station has a negative association with it, while the effects of most built environment factors become insignificant in zero-inflated beta regression. Based on these findings, policy implications are drawn regarding the partnership between transit agencies and ridesourcing companies, the spatially differentiated policies in the central and suburban areas, and the potential problems in providing ridesourcing service to the economically disadvantaged population.
-
Deep Neural Networks for Choice Analysis: Architecture Design with Alternative-Specific Utility Functions
Transportation Research Part C(2020)Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN’s interpretability and predictive power, and to identify effective regularization methods for specific tasks. To address these challenges, this study demonstrates the use of behavioral knowledge for designing a particular DNN architecture with alternative-specific utility functions (ASU-DNN) and thereby improving both the predictive power and interpretability. Unlike a fully connected DNN (F-DNN), which computes the utility value of an alternative k by using the attributes of all the alternatives, ASU-DNN computes it by using only k's own attributes. Theoretically, ASU-DNN can substantially reduce the estimation error of F-DNN because of its lighter architecture and sparser connectivity, although the constraint of alternative-specific utility can cause ASU-DNN to exhibit a larger approximation error. Empirically, ASU-DNN has 2-3% higher prediction accuracy than F-DNN over the whole hyperparameter space in a private dataset collected in Singapore and a public dataset available in the R mlogit package. The alternative-specific connectivity is associated with the independence of irrelevant alternative (IIA) constraint, which as a domain-knowledge-based regularization method is more effective than the most popular generic-purpose explicit and implicit regularization methods and architectural hyperparameters. ASU-DNN provides a more regular substitution pattern of travel mode choices than F-DNN does, rendering ASU-DNN more interpretable. The comparison between ASU-DNN and F-DNN also aids in testing behavioral knowledge. Our results reveal that individuals are more likely to compute utility by using an alternative’s own attributes, supporting the long-standing practice in choice modeling. Overall, this study demonstrates that behavioral knowledge can guide the architecture design of DNN, function as an effective domain-knowledge-based regularization method, and improve both the interpretability and predictive power of DNN in choice analysis. Future studies can explore the generalizability of ASU-DNN and other possibilities of using utility theory to design DNN architectures.
-
Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation
Transportation Research Part C: Emerging Technologies(2020)While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic information from DNNs. This paper demonstrates that DNNs can provide economic information as complete as classical discrete choice models (DCMs). The economic information includes choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, probability derivatives, elasticities, marginal rates of substitution, and heterogeneous values of time. Unlike DCMs, DNNs can automatically learn utility functions and reveal behavioral patterns that are not prespecified by domain experts, particularly when the sample size is large. However, the economic information obtained from DNNs can be unreliable when the sample size is small, because of three challenges associated with the automatic learning capacity: high sensitivity to hyperparameters, model non-identification, and local irregularity. The first challenge is related to the statistical challenge of balancing approximation and estimation errors of DNNs, the second to the optimization challenge of identifying the global optimum in the DNN training, and the third to the robustness challenge of mitigating locally irregular patterns of estimated functions. To demonstrate the strength and challenges, we estimated the DNNs using a stated preference survey from Singapore and a revealed preference data from London, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs. We found that the economic information either aggregated over trainings or population is more reliable than the disaggregate information of the individual observations or training, and that larger sample size, hyperparameter searching, model ensemble, and effective regularization can significantly improve the reliability of the economic information extracted from the DNNs. Future studies should investigate the requirement of sample size, better ensemble mechanisms, other regularization and DNN architectures, better optimization algorithms, and robust DNN training methods to address DNNs’ three challenges to provide more reliable economic information for DNN-based choice models.
-
Are cities prepared for autonomous vehicles? Planning for technological change by U.S. local governments
Journal of the American Planning Association(2019)Problem, Research Strategy, and Findings: Local government policies could impact how autonomous vehicle (AV) technology is deployed. This paper examines how municipalities are planning for AVs, identifies local characteristics that are associated with preparation, and describes what impacts bureaucrats expect from the vehicles. We review existing plans of the United States’ 25 largest cities and survey transportation and planning officials from 120 cities, representative of all municipalities with populations larger than 100,000. First, we find that few local governments have commenced planning for AVs. Second, cities with larger populations and higher population growth are more likely to be prepared. Third, while local officials are optimistic about the technology and its potential to increase safety while reducing congestion, costs, and pollution, more than a third of respondents worried about AVs increasing vehicle-miles traveled and sprawl while reducing transit ridership and local revenues. Those concerns are associated with greater willingness to implement AV regulations, but there is variation among responses depending on political ideology, per-capita government expenditures, and population density.
Takeaway for Practice: Municipal governments’ future approaches to AV preparation will likely depend on characteristics of city residents and local resources. Planners can maximize policy advancement if they work with officials in other cities to develop best practices and articulate strategies that overlap with existing priorities, such as reducing pollution and single-occupancy commuting.
Cited as: "Yonah Freemark, Anne Hudson & Jinhua Zhao (2019) Are Cities Prepared for Autonomous Vehicles?,Journal of the American Planning Association, DOI: 10.1080/01944363.2019.1603760"
-
Transit-Oriented Autonomous Vehicle Operation with Integrated Demand-Supply Interaction
Transportation Research Part C(2018)Autonomous vehicles (AVs) represent potentially disruptive and innovative changes to public transportation (PT) systems. However, the exact interplay between AV and PT is understudied in existing research. This paper proposes a systematic approach to the design, simulation, and evaluation of integrated autonomous vehicle and public transportation (AV+PT) systems. Two features distinguish this research from the state of the art in the literature: the first is the transit-oriented AV operation with the purpose of supporting existing PT modes; the second is the explicit modeling of the interaction between demand and supply. We highlight the transit-orientation by identifying the synergistic opportunities between AV and PT, which makes AVs more acceptable to all the stakeholders and respects the social-purpose considerations such as maintaining service availability and ensuring equity. Specifically, AV is designed to serve first-mile connections to rail stations and provide efficient shared mobility in low-density suburban areas. The interaction between demand and supply is modeled using a set of system dynamics equations and solved as a fixed-point problem through an iterative simulation procedure. We develop an agent-based simulation platform of service and a discrete choice model of demand as two subproblems. Using a feedback loop between supply and demand, we capture the interaction between the decisions of the service operator and those of the travelers and model the choices of both parties. Considering uncertainties in demand prediction and stochasticity in simulation, we also evaluate the robustness of our fixed-point solution and demonstrate the convergence of the proposed method empirically. We test our approach in a major European city, simulating scenarios with various fleet sizes, vehicle capacities, fare schemes, and hailing strategies such as in-advance requests. Scenarios are evaluated from the perspectives of passengers, AV operators, PT operators, and urban mobility system. Results show the trade off between the level of service and the operational cost, providing insight for fleet sizing to reach the optimal balance. Our simulated experiments show that encouraging ride-sharing, allowing in-advance requests, and combining fare with transit help enable service integration and encourage sustainable travel. Both the transit-oriented AV operation and the demand-supply interaction are essential components for defining and assessing the roles of the AV technology in our future transportation systems, especially those with ample and robust transit networks.
-
Information Effect on Autonomous Vehicle Mode Choice: A Randomized Control Trial Experiment
Transportation Research Board 97th Annual Meeting(2018)This paper studies the effect of negative safety-related information on autonomous vehicle (AV) mode choice in Singapore. The authors implemented a dynamic online survey with half of the subjects receiving negative safety information about AV via a randomized control trial. The authors use a video of Tesla’s fatal crash under autopilot as an approximation of the negative safety information of AV. The authors test the impact of watching this video and its interaction with prior knowledge of AV technology and safety on changes in mode choice stated preference. The survey integrates revealed preference (RP) and stated preference (SP) scenarios, by generating the SP data directly based on the reported RP values. A nested logit modeling framework is built to jointly estimate the RP and SP survey data (N~2000). The modeling results reveal that the information effect of the crash video varies largely by sociodemographic group. Subjects with high income and with high education are less influenced by the information, but in different ways. High income respondents are more tractable when receiving new information compared with their prior knowledge of AV safety, but highly educated respondents are more influenced if the information is consistent with and an enhancement of their prior knowledge. The fading effect—if the impact of negative information persists over time—is also studied. The immediate effect on a young, high-income, full-time employee with graduated degree fades easily, while the impact on the subjects in low-income low-education and older groups may persist over time.
-
Impacts of remote work on vehicle miles traveled and transit ridership in the USA
Nature Cities(2024)Remote work's potential as a sustainable mobility solution has garnered attention, particularly due to its widespread adoption during the COVID-19 pandemic. Our study systematically examines the impacts of remote work on vehicle-miles traveled (VMT) and transit ridership in the United States from April 2020 to October 2022. We find that using the pre-pandemic levels as the baselines, a mere 1% decrease in on-site workers corresponds to a 0.99% reduction in state-level VMT and a 2.26% drop in Metropolitan Statistical Area (MSA)-level transit ridership. Notably, a 10% decrease in on-site workers compared to the pre-pandemic level could yield a consequential annual reduction of 191.8 million metric tons (10%) in CO2 emissions from the transportation sector, alongside a substantial $3.7 billion (26.7%) annual loss in transit fare revenues within the contiguous US. These findings offer policymakers crucial insights into how different remote work policies can impact urban transport and environmental sustainability as remote work continues to persist.
-
E-scooter sharing to serve short-distance transit trips: a Singapore Case
Transportation Research Part A(2021)E-scooter sharing provides a last-mile solution to complement transit services, but less was known about its effectiveness in serving short-distance transit trips. We investigate the potential of using e-scooter sharing to replace short-distance transit trips of excessive indirectness, multiple transfers, and long access-egress walking. First, we conducted a stated preference survey on e-scooter users in the Central Area of Singapore and estimated mixed logit models to examine factors influencing the choice of e-scooters and transit. We then calculated the number of transit trips that can be replaced by e-scooters. Second, we analyzed the decision of e-scooter companies in terms of the trade-offs between serving more e-scooter trips and making more revenue under varying fares. The results show that fare, MRT transfer, and MRT access-egress walking distance have significantly negative impacts on mode utilities with random tastes among respondents. Male, young and high-income groups are more heterogeneous in e-scooter preferences compared with other groups. The loss of mode share can be nearly 17% if maximizing the revenue. We classify trade-off situations into five categories and provide suggestions of how to balance between mode share and revenue for each category. Several implications are drawn for better harnessing and regulating this new mobility service, including where to deploy e-scooters to satisfy the demand unmet by the transit and how to reach a proper balance between private operators and public welfare.
-
Measuring policy leakage of Beijing’s car ownership restriction
Transportation Research Part A: Policy and Practice(2021)In response to severe traffic congestion and air pollution, Beijing introduced a car ownership restriction policy to curb growth in the number of private cars in the city. However, Beijing residents can still purchase and register their cars in neighboring cities and this “leakage” may substantially reduce the policy’s effectiveness. Using city-level data collected from the CEIC China Premium Database, we aim to quantify the spill-over effect: the impact of Beijing’s policy on the growth of private car registrations in neighboring cities. We first deploy a synthetic control method to create a weighted combination of non-treated cities for each treated city. We then employ a difference-in-differences approach to estimate the policy leakage. Our models suggest that the policy resulted in additional 443,000 cars sold in the neighboring cities (within 500 km of Beijing) from 2011-2013, compared to if the policy had not been implemented. 35%-40% of the car growth reduction stipulated by the policy simply spilled over to neighboring cities. The significance of the policy leakage necessitates positioning Beijing’s urban transportation in a broader context and executing regional collaboration.
-
Adoption of Exclusive and Pooled TNC Services in Singapore and the U.S.
ASCE Journal of Transportation Engineering, Part A: Systems146,(2020)On-demand mobility services provided by transport network companies (TNCs) have experienced significant growth in their adoption and diversification of services in major metropolitan cities around the world. This study analyzed primary data from Singapore to explore the sociodemographics of TNC users and determine who among TNC users is more likely to pool their trips and what modes these services are replacing. We compared these results with a comprehensive literature review of similar studies of TNC users in the metropolitan US. We found that the sociodemographics of TNC users in general are similar in Singapore and the US: younger, highly educated, and higher income individuals are more likely to have used TNC services. On the other hand, when differentiating by type of TNC service, we found that younger individuals from households that do not own a car are more likely to have pooled in Singapore, whereas employment is an important predictor in the US. We also found differences in mode substitution; whereas TNC trips in the US primarily induce additional trips or replace trips by public and non-motorized transport, in Singapore they primarily replace personal/private vehicle trips. In Singapore, we explored mode substitution by exclusive and pooled TNC services separately, and found that pooled trips draw more from public and nonmotorized transport, whereas exclusive trips replace more personal/private vehicle trips. These results suggest that people in Singapore view exclusive and pooled TNC services as distinct travel options that may be more closely related to other private or public transport, respectively. Differences between Singapore and the US highlight the importance of accounting for local context and suggest that the quality of all travel alternatives in the urban area will affect the mode substitution of TNC trips.
-
Risk Preference and Adoption of Autonomous Vehicles
Transportation Research Part A(2019)While there is an increasingly large body of research on the potential demand for autonomous vehicles (AV), an understudied factor is people’s risk preference. Risk preference is important because many aspects of AVs are highly uncertain as the technology and its encompassing mobility system emerge and continue to evolve. This study analyzed how risk preferences influence the choice of AVs, and how risk preferences elicited by economic and psychometric methods differ in their impacts. We conducted a stated preference survey in Singapore with 1,303 persons and 7 choice scenarios per person. We extracted two economic risk preference parameters based on prospect theory using individualized linear regressions; and we extracted one psychometric risk preference parameter based on a set of Likert scale questions using factor analysis. After applying mixed logit models incorporating the risk preference parameters, we found that risk-seeking preference significantly increases the choice of AVs. The economic risk preference and the psychometric risk preference are statistically uncorrelated; both contribute to predicting AV usage, and the economic risk measure improves the choice model more than the psychometric one. The results show that people’s risk preference is an important factor influencing the adoption of AVs, and future studies should continue to examine the specific relationship between the multiple components of risk preferences and the multiple uncertain aspects of AVs.
-
Enabling Transit Service Quality Co-monitoring Through a Smartphone-Based Platform
Transportation Research Record: Journal of the Transportation Research Board(2017)The growing ubiquity of smartphones offers public transit agencies an opportunity to transform ways to measure, monitor, and manage service performance. We demonstrate the potential in a new tool for actively engaging customers in measuring satisfaction and co-monitoring bus service quality. The pilot initiative adapted a smartphone-based travel survey system, Future Mobility Sensing (FMS), to collect real-time customer feedback and objective operational measurements on specific bus trips. The system uses a combination of GPS, Wi-Fi, Bluetooth, and cellphone accelerometer data to track transit trips, while soliciting users’ feedback on trip experience. While not necessarily intended to replace traditional monitoring channels and processes, these data can complement official performance monitoring through a more customer-centric perspective in relative real-time. The pilot operated publicly for three months on Boston’s Silver Line (SL) bus rapid transit, in collaboration with the Massachusetts Bay Transportation Authority (MBTA). Seventy-six participants completed the entrance survey, half of whom actively participated, completing over 500 questionnaires while on board, at the end of a trip and/or at the end of a day. Participation was biased towards frequent SL users, who were majority White and of higher income. Indicative models of user reported satisfaction reveal some interesting relationships, but the models can be improved by fusing the app-collected data with performance characteristics obtained through the automatic vehicle location system. Broader and more sustained user engagement remains a critical future challenge.
-
Attitudes Toward Effective Time Use in Autonomous Mobility-on-Demand Services in Singapore
Transportation Research Board 97th Annual Meeting(2018)How autonomous vehicles (AVs) may impact passengers’ travel time use is not well understood. Because AVs require minimal attention, users may find themselves free to engage in new activities and use their travel time more effectively. If AVs improve the quality of activities conducted while traveling, passengers may experience reduced value of travel time savings, leading to more and longer trips and thereby increasing congestion and emissions. On the other hand, if people are able to more effectively engage in productive work activities while traveling, overall societal productivity may increase with the introduction of this new technology. Despite the large and varied implications, there has been little systematic investigation into how people will use their time in AVs. This paper presents the results of an online survey conducted among 1,782 Singapore commuters. The authors use several metrics of time use effectiveness to analyze the activities which people currently engage in on their commute, and report respondents’ attitudes towards time use both presently and in AVs. The authors also test whether respondents would significantly change their commuting activities if they were instead to commute using an autonomous mobility-on-demand service. The authors' findings suggest automobile commuters are more likely to engage in leisure activities in AVs, while public transit riders are likely to conduct fewer activities overall, but to participate in more social activities such as talking face-to-face. AVs therefore appear to allow for more effective leisure done during the commute, but may not lead to gains in productive work done while traveling
-
Understanding the Usage of Stationless Bike Sharing in Singapore
International Journal of Sustainable Transportation(2018)A new generation of bike-sharing services without docking stations is currently revolutionizing the traditional bike-sharing market as it dramatically expands around the world. This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service. We collected the GPS data of all dockless bikes from one of the largest bike sharing operators in Singapore for nine consecutive days, for a total of over 14 million records. We adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period. The models explored the impact of bike fleet size, surrounding built environment, access to public transportation, bicycle infrastructure, and weather conditions on the usage of dockless bikes. Larger bike fleet is associated with higher usage but with diminishing marginal impact. In addition, high land use mixtures, easy access to public transportation, more supportive cycling facilities, and free-ride promotions positively impact the usage of dockless bikes. The negative influence of rainfall and high temperatures on bike utilization is also exhibited. The study also offered some guidance to urban planners, policy makers, and transportation practitioners who wish to promote bike-sharing service while ensuring its sustainability.
-
Rebalancing Shared Mobility-on-Demand Systems: a Reinforcement Learning Approach
IEEE ITSC Workshop on Intelligent Public Transport 2017(2017)Shared mobility-on-demand systems have very promising prospects in making urban transportation efficient and affordable. However, due to operational challenges among others, many mobility applications still remain niche products. This paper addresses rebalancing needs that are critical for effective fleet management in order to offset the inevitable imbalance of vehicle supply and travel demand. Specifically, we propose a reinforcement learning approach which adopts a deep Q network and adaptively moves idle vehicles to regain balance. This innovative model-free approach takes a very different perspective from the state-of-the-art network-based methods and is able to cope with large-scale shared systems in real time with partial or full data availability. We apply this approach to an agent based simulator and test it on a London case study. Results show that, the proposed method outperforms the local anticipatory method by reducing the fleet size by 14% while inducing little extra vehicle distance traveled. The performance is close to the optimal solution yet the computational speed is 2.5 times faster. Collectively, the paper concludes that the proposed rebalancing approach is effective under various demand scenarios and will benefit both travelers and operators if implemented in a shared mobility-on-demand system.
-
An Urban Agenda for Autonomous Vehicles: Embedding Planning Principles into Technological Deployment
Transportation Research Board 97th Annual Meeting(2018)The deployment of autonomous vehicles (AVs) has spawned a considerable literature on the role of national and state-level governments in regulating components of AV manufacturing, emissions, safety, licensing, and data sharing. These provide insight into how AVs can be integrated into the current transportation system. Yet the potential for local governments to shape their futures through AV policies is underexplored. This paper argues that it is both necessary and feasible for local government to adjust local mobility policies for AVs towards the goal of achieving key planning principles by disrupting the current transportation system. Local governments must leverage the ephemeral moment in advance of full-scale AV rollout to achieve the principles of equitable, environmentally sustainable, efficient, and livable cities. It is necessary to establish a new regulatory relationship with automobiles and design mobility policies to cultivate AV benefits, while responding to their potentially deleterious impacts. Local governments are capable of doing so through their already-existing regulatory mechanisms managing much of the transportation infrastructure, public transit, taxi, parking, land uses, and public data. Based on such local government power, we identify eight policy instruments that are feasible: centralized data collection and distribution; distance- and congestion-based road pricing; integration of AV and transit networks; income-based subsidies; minimum levels of service provision; zero-emission vehicles; lowered parking provision; and a rethinking of the use of street space. We explore how such instruments could be implemented in the context of existing regulatory mechanisms through the diverging lenses of indicative cases of Chicago and London.
-
Distributional Effects of Lotteries and Auctions —License Plate Regulations in Guangzhou
Transportation Research Part A: Policy and Practice106,(2017)Lotteries and auctions are common ways of allocating public resources, but they have rarely been used simultaneously in urban transportation policies. This paper presents a unique policy experiment in Guangzhou, China, where lotteries and auctions are used in conjunction to allocate vehicle licenses. Guangzhou introduced vehicle license regulations to control the monthly quota of local automobile growth in 2012. To obtain a license, residents are required to choose between the lottery and auction method. Since the introduction of the regulations, there has been heated debates on the distributional effects of lotteries and auctions; however, the debates have not been grounded in empirical studies. We analyze the distributional effects of such mixed mode of resource allocation in a positive manner based on individual behavioral choices. We conducted a survey in January 2016 (n = 1,000 people * 12 months), and used mixed logit models to analyze how socio-economic status, including income and automobile ownership, determined people’s choices among lottery, auction, and non-participation alternatives. We find that income increased participation, but did not influence non-car owners’ choices between lotteries and auctions, which contrasts with the common notion that lotteries benefit the poor. Additionally, the positive impact of car ownership on participation indicates a car-dependent trajectory for automobile growth. The significant socio-economic differentiators between lotteries and auctions were age, gender, and education. Proxies of mobility needs were insignificant overall. The program attributes had a much larger impact than all other variables—people were more likely to choose lotteries with higher winning rates and more participants and more likely to choose auctions with higher prices and more participants. We concluded that for those who participated, the choice between lotteries and auctions did not depend on their income or mobility needs but, rather, the probability of winning plates and the opportunity for speculation.
-
The Tradeoff Between Efficiency and Fellow Passenger Preference: a Preference-Based Ridesharing Model
Transportation Research Board 96th Annual Conference(2017)Advances in information technology are enabling the growth of real-time ridesharing—whereby passengers are paired up on car trips to improve system efficiency by using fewer cars. Lesser known, however, are the opportunities of shared mobility as a tool to foster and strengthen human interactions. The nature of shared car rides is impromptu, captive for a considerable duration, and remarkably more intimate, representing a unique juxtaposition of spontaneity and intensity. While ridesharing services optimize the matching of fellow passengers based on efficiency criteria, like maximal paired trips, or minimal VMT, they do not consider passengers’ preference for each other—or only use it as a restriction—especially for the preference from sociodemographic characteristics, like gender. We propose a preference-based matching model, which optimizes fellow passenger pairing by using the most stably favorable fellow passengers as a system objective, and evaluate the tradeoff between it and efficiency-based matching models. In the model, we implement two types of synthetic preference: 1) random preference, to show efficacy of the model and the range of tradeoff between different matching schemes, and 2) group-based preference of five scenarios, to illustrate how preference coming from sociodemographic characteristics lead to different matching outcomes. We use the taxi trips of Manhattan to put this model in real-world scenarios. The results of the paper show that compared to the matchings under different efficiency-based optimization goals, the preference-based model can increase the average ranking of paired fellow passenger to the top of preference lists at the compensation of only a moderate amount of efficiency loss. The model can be used for ridesharing service design, and as a reference for policy makers of ridesharing regulation.
-
Simulating the First Mile Service to Access Train Stations by Shared Autonomous Vehicle
Transportation Research Board 96th Annual Meeting(2017)This paper studies the potential impacts of autonomous vehicle (AV) sharing with mobility-on demand service on the public transit system. We analyze the current travel demand in the public transit system in Singapore with a special focus on the first-/last-mile problem during morning peak hours. The first-/last-mile in this paper is defined as the gap between origin/destination and the heavy rail stations. A feasible method to integrate AV sharing in current transit system is proposed, which is to use on-demand AV sharing service as the alternative to the low-demand buses to improve the first-/last-mile connectivity in the study area. An agent-based simulation model is built to evaluate the performance of the new integrated service. The simulation models the behaviors, movements, and interactions of the agents—passengers, AVs, and traditional buses. A bus-only scenario is firstly simulated for validation purpose based on the real-world statistics. Then a series of scenarios integrating AV sharing in public transit system with various fleet size and passenger’s sharing preference are simulated. The results under the AV sharing scenario show that, by letting everyone in the system share their last-mile rides, with careful selection of the size of AV fleet, the new service is able to (1) reduce the average out-of-vehicle time for the passengers, (2) occupy less road resources than the low-demand buses, and (3) have a higher possibility to be financially viable.
People
-
Banani Anuraj
Research Engineer SMART -
Yu Shen
Assistant Professor at Tongji Univ. -
Shenhao Wang
Postdoctoral Associate -
Hongmou Zhang
Postdoctoral Associate -
Zhan Zhao
PhD 2018 -
Zhejing Cao
Visiting PhD Candidate -
Hui Kong
Postdoctoral Associate -
Donghang Li
MST Student -
Yuebing Liang
Postdoctoral Associate -
Rachel Luo
MCP/MST 2021 -
Baichuan Mo
PhD 2022 -
Xin Yang
Postdoctoral Associate -
Yunhan Zheng
Postdoctoral Associate -
Jinhua Zhao
Professor of Cities and Transportation