Jinhua Zhao | Massachusetts Institute of Technology (MIT) (original) (raw)

Papers by Jinhua Zhao

Other repository, 2016

Increases in ridership are outpacing capacity expansions in a number of transit systems. By shift... more Increases in ridership are outpacing capacity expansions in a number of transit systems. By shifting their focus to demand management, agencies can instead influence how customers use the system, getting more out of their present capacity. This paper uses Hong Kong's MTR system as a case study to explore the effects of crowding-reduction strategies and how to use fare data to support these measures. MTR introduced a discount in September 2014 to encourage users to travel before the peak and reduce on-board crowding. To understand the impacts of this intervention, first, existing congestion patterns were reviewed and a clustering analysis was used to reveal typical travel patterns among users. Then, changes to users' departure times were studied at three levels to evaluate the promotion's effects. Patterns among all users were measured across both the whole system and for specific rail segments. The travel patterns of the user groups, who have more homogeneous usage characteristics, were also evaluated, revealing groups to have differing responses to the promotion. The incentive was found to have impacted morning travel, particularly at the beginning of the peak hour and among users with commuter-like behavior. Aggregate and group-specific elasticities were developed to inform future promotions and the results were also used to suggest other potential incentive designs.

Transportation Research Board 97th Annual MeetingTransportation Research Board, 2018

Transportation Research Board 98th Annual MeetingTransportation Research Board, 2019

Transportation Research Part A: Policy and Practice, 2021

Abstract In response to severe traffic congestion and air pollution, Beijing introduced a car own... more Abstract 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 to 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.

Journal of Urban Technology, 2020

One of the main features in mobility sharing applications is the exposure of personal data provid... more One of the main features in mobility sharing applications is the exposure of personal data provided to the system. Transportation and location data can reveal personal habits, preferences, and behaviors, and riders could be keen not to share the exact location of their origin and/or destination. But what is the price of privacy in terms of decreased efficiency of the mobility sharing system? In this paper, for the first time, we address the privacy issues under this point of view, and show how location privacy-preserving techniques could affect the performance of mobility sharing applications, in terms of both System Efficiency and Quality of Service. To this extent, we first apply different data-masking techniques to anonymize geographical information, and then compare the performance of shareability networks-based trip matching algorithms for ride-sharing, applied to the real data and to the privacypreserving data. The goal of the paper is to evaluate the performance of mobility sharing privacy-preserving systems, and to shed light on the trade-off between data privacy and its costs. The results show that the total traveled distance increase due to the introduction of data privacy could be bounded if users are willing to spend (or "pay") for more time in order to share a trip, meaning that data location privacy impacts both efficiency and quality of service.

Transportation, 2018

Beyond their functional purpose, cars are often considered a status symbol. There may exist a cer... more Beyond their functional purpose, cars are often considered a status symbol. There may exist a certain level of pride associated with owning and using cars, especially in regions where motorization is rapidly growing. However, there is little empirical evidence in terms of how car pride is related to different behavioral aspects, such as car ownership and use, especially in the context of developing countries. This paper presents an exploration of car pride and its association with car-related behavior. In this work, car pride is defined as the self-conscious emotion derived from the appraisal of owning and using cars as a positive self-representation. It pertains to both the symbolic and affective functions of the car. Using survey data (n = 1389) from Shanghai, China, we empirically measure car pride as a latent variable based on five Likertscale statements and test the association of car pride with car use, vehicle preferences, and car ownership. Based on two structural equation models, we show that: (1) car pride is positively correlated with car use; (2) car pride correlates significantly with owning newer, more expensive, and luxury cars, and Shanghai's more expensive local car licenses; (3) car owners in general have higher car pride than non-owners; and (4) car pride is largely independent of one's socioeconomic characteristics. Although the analysis focuses on Shanghai, the findings of the positive correlation between car pride and behavior are consistent with prior studies in developed countries. These findings highlight the importance of car pride regarding multiple behavioral aspects of car ownership and use and its potential impact on mobility management.

Transport, 2015

A bus may be blocked from entering and exiting a stop by other buses and traffic lights. The obje... more A bus may be blocked from entering and exiting a stop by other buses and traffic lights. The objective of this paper is to model each type of delay under these phenomena and the overall delay a bus experiences at a stop. Occupy-based delay, transfer block-based delay and block-based delay are defined and modelled. Bus delay at stop is just the sum of these three types of delay. Bus arrival rate, bus service rate, berth number and traffic lights are taken into consideration when modelling delay. Occupy-based delay is modelled with mean waiting time in Queueing theory. Transfer block-based delay and block-based delay are modelled based on standard deviation of waiting time and the probabilities of their occurrences. Two stops in Vancouver, Canada are selected for parameter estimation and model validation. The unknown parameter is estimated as 0.4230 using Ordinary Least Squares (OLS), which indicates that 42.3% of waiting time variation can be attributed to buses being blocked by the ...

Transportation Research Part C: Emerging Technologies, 2018

For intelligent urban transportation systems, the ability to predict individual mobility is cruci... more For intelligent urban transportation systems, the ability to predict individual mobility is crucial for personalized traveler information, targeted demand management, and dynamic system operations. Whereas existing methods focus on predicting the next location of users, little is known regarding the prediction of the next trip. The paper develops a methodology for predicting daily individual mobility represented as a chain of trips (including the null set, no travel), each defined as a combination of the trip start time t, origin o, and destination d. To predict individual mobility, we first predict whether the user will travel (trip making prediction), and then, if so, predict the attributes of the next trip t o d (, ,) (trip attribute prediction). Each of the two problems can be further decomposed into two subproblems based on the triggering event. For trip attribute prediction, we propose a new model, based on the Bayesian n-gram model used in language modeling, to estimate the probability distribution of the next trip conditional on the previous one. The proposed methodology is tested using the pseudonymized transit smart card records from more than 10,000 users in London, U.K. over two years. Based on regularized logistic regression, our trip making prediction models achieve median accuracy levels of over 80%. The prediction accuracy for trip attributes varies by the attribute considered-around 40% for t, 70-80% for o and 60-70% for d. Relatively, the first trip of the day is more difficult to predict. Significant variations are found across individuals in terms of the model performance, implying diverse travel behavior patterns.

Transportation Research Part A: Policy and Practice, 2020

Previous studies have explored the relationships between an individual's use of information and c... more Previous studies have explored the relationships between an individual's use of information and communication technology (ICT) and their travel. However, these studies often focus on one specific type of travel and have not considered new forms of mobility, such as ride-hailing, that are enabled by greater ICT penetration. This paper focuses on how ICT use impacts an individual's self-reported travel behavior-including total number of trips, personal miles traveled (PMT), and vehicle miles traveled (VMT) in a typical travel day-and ride-hailing use in the past month. Specifically, we investigate whether substitution or complementarity dominates the relationships between ICT use and an individual's net travel; how ICT impacts individual ride-hailing adoption and frequency of use; and how ride-hailing use is associated with an individual's overall travel behavior. Using data from the 2017 U.S. National Household Travel Survey (NHTS), we estimate a structural equation model that includes a robust set of individual, household, built environment, and travel characteristics, frequency of ICT use, and a hurdle model (two-part regression) of the adoption and frequency of ride-hailing use. Results reveal that greater ICT is not significantly related to the total number of trips that an individual takes, but it does significantly predict higher PMT and VMT. Greater ICT use is positively and substantively correlated with whether or not the individual has used ride-hailing in the past 30 days, but has no significant relationship with the frequency of ride-hailing use with this bounded outcome being controlled for. We further find that an individual's ride-hailing use has a small negative correlation with their PMT and VMT after controlling for other common factors. Our results indicate the importance of future research examining the mechanisms by which ICT use increases the distance individuals travel and the role that new ICT-enabled modes, such as ridehailing, play in changing these mechanisms at both the individual and system levels.

Urban Science, 2020

Local governments play an important role in structuring urban transportation through street desig... more Local governments play an important role in structuring urban transportation through street design, zoning, and shared jurisdiction over ride-hailing, transit, and road pricing. While cities can harness these powers to steer planning outcomes, there is little research about what local officials think about regulatory changes related to autonomous vehicles (AV). We compile key AV-related policies recommended by scholars but rarely implemented, and conduct a survey of municipal officials throughout the United States, exploring their personal support and perceptions of bureaucratic capacity, legal limits, and political backing for each policy. This paper finds broad personal support for regulations related to right-of-way, equity, and land use, such as for increasing pedestrian space, expanding access for low-income people, and reducing sprawl. However, officials emphasized uncertain bureaucratic and legal capacity for city intervention outside of these areas, reaffirming limited local...

Transportation Research Part C: Emerging Technologies, 2020

Although automatically collected human travel records can accurately capture the time and locatio... more Although automatically collected human travel records can accurately capture the time and location of human movements, they do not directly explain the hidden semantic structures behind the data, e.g., activity types. This work proposes a probabilistic topic model, adapted from Latent Dirichlet Allocation (LDA), to discover representative and interpretable activity categorization from individual-level spatiotemporal data in an unsupervised manner. Specifically, the activity-travel episodes of an individual user are treated as words in a document, and each topic is a distribution over space and time that corresponds to certain type of activity. The model accounts for a mixture of discrete and continuous attributes-the location, start time of day, start day of week, and duration of each activity episode. The proposed methodology is demonstrated using pseudonymized transit smart card data from London, U.K. The results show that the model can successfully distinguish the three most basic types of activities-home, work, and other, and it fits the data significantly better than rule-based approaches. As the specified number of activity categories increases, more specific subpatterns for home and work emerge. This work makes it possible to enrich human mobility data with representative and interpretable activity patterns without relying on predefined activity categories or heuristic rules.

Transportation Research Part B: Methodological, 2018

Although stable in the short term, individual travel behavior generally tends to change over the ... more Although stable in the short term, individual travel behavior generally tends to change over the long term. The ability to detect such changes is important for product and service providers in continuously changing environments. The aim of this paper is to develop a methodology that detects changes in the patterns of individual travel behavior from vehicle global positioning system (GPS)/global navigation satellite system (GNSS) data. For this purpose, we first define individual travel behavior patterns in two dimensions: a spatial pattern and a frequency pattern. Then, we develop a method that can detect such patterns from GPS/GNSS data using a clustering algorithm. Finally, we define three basic pattern-change scenarios for individual travel behavior and introduce a pattern-matching metric for detecting these changes. The proposed methodology is tested using GPS datasets from three randomly selected anonymous users, collected by a Chinese automotive manufacturer. The results show that our methodology can successfully identify significant changes in individual travel behavior patterns.

Case Studies on Transport Policy, 2019

The authors would like to thank all of the subjects of our interviews for their time and detailed... more The authors would like to thank all of the subjects of our interviews for their time and detailed commentary. We also acknowledge our colleagues in the MIT JTL Urban Mobility Lab who have contributed their critical commentary and support to this work, particularly Shenhao Wang and Xuenan Ni. This work was supported through the MIT Energy Initiative's Mobility of the Future study.

IEEE Transactions on Intelligent Transportation Systems, 2021

Recent technological developments have shown significant potential for transforming urban mobilit... more Recent technological developments have shown significant potential for transforming urban mobility. Considering first-and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.

Transportation Research Part B: Methodological, 2021

Advances in behavioral economics have begun to provide a new toolkit of theories, models, and emp... more Advances in behavioral economics have begun to provide a new toolkit of theories, models, and empirical methods for designing and evaluating policy. While many of these techniques are highly relevant to behavioral problems that planners encounter when consulting with the public, crafting policy and regulations, and promoting sustainable patterns of behavior, it has received only limited attention in the planning and transportation literature. The authors review this literature and present a framework for generating, implementing, and testing the results of different interventions designed to affect users’ travel behavior by delivering behavioral feedback via an activity-tracking smartphone application. The results of this promotional strategy are tested in two pilot projects among university students and “Bike to Work Week” participants in British Columbia and Minnesota. Implications for program evaluation and funding and future directions for research on behavioral interventions ar...