Public Transit Planning and Operation in the Era of Automation, Electrification, and Personalization

With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, we develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers’ mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, our optimized transit schedules consistently lead to 0.4%–3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers’ preferences—by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win–win–win outcome.

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Public transit network planning in small cites considering safety and convenience

Advances in Mechanical Engineering ◽

10.1177/1687814020902351 ◽

2020 ◽

Vol 12 (1) ◽

pp. 168781402090235 ◽

Cited By ~ 5

Author(s):

Changxi Ma ◽

Dong Yang

Keyword(s):

Traffic Congestion ◽

Public Transit ◽

Network Planning ◽

Optimization Method ◽

Transit Oriented Development ◽

Transit Planning ◽

Transit Network ◽

The Public ◽

Traffic Generation ◽

Interval Value

Scientific and rational public transit network planning, not only can effectively alleviate city traffic congestion, but also can reduce the risk of accidents. First, based on the data of residents’ travel survey, this article employs the multiple regression method to forecast the traffic generation and adopts the double-constrained gravity model to forecast the residents’ travel distribution of small cites. Second, by aiming at public transit planning objectives, the unsafe roads for public transit are screened, and the public transit trip-mode sharing rate is set as the interval value. According to the interval value, the public transit trip-mode sharing rate is divided into three cases, and the three alternatives of public transit network are calculated based on the network optimization method and the public transit-oriented development model. Next, the alternatives are evaluated by the set pair analysis method, and the optimal scheme is selected. Finally, this article takes the public transit network planning of Huaiyuan County in Anhui Province as an example, and the results show the proposed method is feasible.

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Urban Public Transit Planning Strategy in the Era of Decreasing Transport Demand

Urban Public Transportation System ◽

10.1061/40717(148)20 ◽

2004 ◽

Author(s):

Keiichi Kishino

Keyword(s):

Public Transit ◽

Transit Planning ◽

Planning Strategy

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How is public transit in the megacity peripheral relocatees’ area in China? Captive transit rider and dynamic modal accessibility gap analytics in a peripheral large-scale residential area in Shanghai, China

Journal of Transport and Land Use ◽

10.5198/jtlu.2020.1505 ◽

2020 ◽

Vol 13 (1) ◽

pp. 1-21

Author(s):

Jinping Guan ◽

Kai Zhang ◽

Shuang Zhang ◽

Yizhou Chen

Keyword(s):

Data Analytics ◽

Large Scale ◽

Public Transit ◽

Central City ◽

Subjective Evaluation ◽

Big Data Analytics ◽

Heterogeneous Data ◽

Transit Planning ◽

Covered Area ◽

The Government

In the process of Chinese megacity suburbanization, central-city substandard housing is demolished. The government relocates residents to megacity peripheral relocatees’ areas. So far, few studies have focused on captive transit riders and analyzed the dynamic points of interest (POI) accessibility by public transit compared to the private mode in these areas. To fill this gap, this study conducts a survey in Jinhexincheng, one of these areas in Shanghai, China; analyzes captive-transit riders with multiple models; and computes the dynamic modal accessibility gap (DMAG) of public transit and private travel mode using multi-source heterogeneous data. Results show that 71.77% of transit-rider samples acknowledge they “have no other choice and have to travel by transit.” These captive transit riders are more often older, female, non-working, without a driving license, and with more complaints toward public transport. Subjective transit evaluation’s ordinal regression models show that waiting time, speed, operating hours, and number of lines/stops contribute to the low transit subjective evaluation. These things should be given a high priority in transit improvement. As for the captive transit riders, transit’s transfer and bicycle availability should be improved. Using big data analytics, a more fine-grained scale is brought in by computing a DMAG index. It shows a person mostly has a better POI accessibility by private mode for the 30-minute, real-travel-covered area for 24 hours of the average day. For the 60-minute, real-travel-covered area, public transit mostly has a better POI accessibility. This study supports transit planning and decision-making for megacity peripheral relocatees’ areas using multi-source heterogeneous data analytics.

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Identifying Public Transit Commuters Based on Both the Smartcard Data and Survey Data: A Case Study in Xiamen, China

Journal of Advanced Transportation ◽

10.1155/2018/9693272 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Shichao Sun ◽

Dongyuan Yang

Keyword(s):

Machine Learning ◽

Survey Data ◽

Public Transit ◽

Learning Algorithm ◽

Travel Patterns ◽

Transit Planning ◽

Transit System ◽

Location Data ◽

Machine Learning Approach ◽

Transit Use

Understanding the travel patterns of public transit commuters was important to the efforts towards improving the service quality, promoting public transit use, and better planning the public transit system. Smartcard data, with its wide coverage and relative abundance, could provide new opportunities to study public transit riders’ behaviors and travel patterns with much less cost than conventional data source. However, the major limitation of smartcard data is the absence of social attributes of the cardholders, so that it cannot clearly extract public transit commuters and explain the mechanism of their travel behaviors. This study employed a machine learning approach called Naive Bayesian Classifier (NBC) to identify public transit commuters based on both the smartcard data and survey data, demonstrated in Xiamen, China. Compared with existing methods which were plagued by the validation of the accuracy of the identification results, the adopted approach was a machine learning algorithm with functions of accuracy checking. The classifier was trained and tested by survey data obtained from 532 valid questionnaires. The accuracy rate for identification of public transit commuters was 92% in the test instances. Then, under a low calculation load, it identified the objectives in smartcard data without requiring travel regularity assumptions of public transit commuters. Nearly 290,000 cardholders were classified as public transit commuters. Statistics such as average first boarding time and travel frequency of workdays during peak hours were obtained. Finally, the smartcard data were fused with bus location data to reveal the spatial distributions of the home and work locations of these public transit commuters, which could be utilized to improve public transit planning and operations.

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Density and Captivity in Public Transit Success: Observations from the 1995 Nationwide Personal Transportation Study

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1735-02 ◽

2000 ◽

Vol 1735 (1) ◽

pp. 10-18 ◽

Cited By ~ 22

Author(s):

Steve E. Polzin ◽

Xuehao Chu ◽

Joel R. Rey

Keyword(s):

Travel Behavior ◽

Public Transit ◽

Good Time ◽

Transit Planning ◽

Economic Changes ◽

The Public ◽

Transit Market ◽

Personal Transportation ◽

Transit Use ◽

In Transit

The new millennium provides a good time to reflect on transportation-industry trends in some fundamental external factors that influence transportation behavior and planning response. In the public-transit industry, urban density and transit captivity have long been fundamental conditions driving transit planning and service and facility investment decisions. In light of demographic and economic changes, it is useful to revisit the issue of the importance of these factors to the transit market. Findings from a comprehensive analysis of the 1995 Nation-wide Personal Transportation Study (NPTS), which explored current transit-travel behavior, are reported. Two key findings reflect on two historical axioms in transit: ( a) the extent to which density influences transit use and ( b) the importance of the transit-dependent market. The research findings reiterate the significant influence that development density has on public transit mode share and bring to light some revealing data on the influence of urban-area size on transit use. The importance of transit dependency on transit use is documented, and trends in transit dependency over the past few decades are revealed. Finally, the implications of these trends for the public-transit industry are discussed.

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Assessing Impacts of New Subway Stations on Urban Thefts in the Surrounding Areas

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10100632 ◽

2021 ◽

Vol 10 (10) ◽

pp. 632

Author(s):

Chong Xu ◽

Xi Chen ◽

Lin Liu ◽

Minxuan Lan ◽

Debao Chen

Keyword(s):

Public Transit ◽

Control Area ◽

Transit Planning ◽

Spring Festival ◽

New Public ◽

Before And After ◽

Subway System ◽

The Difference ◽

Subway Stations ◽

Surrounding Areas

Whether newly implemented public transit stations influence the nearby crime pattern has been debated for years. In ZG City, China, 2 new subway lines and 20 new stations were implemented in 2017. This intervention allows us to test the plausible relationship between new public transit stations and thefts in the surrounding areas. We use the difference-in-differences (DID) model to assess the theft in the treatment and control areas before and after the implementation of the new stations, with necessary socioeconomic and land-use variables and time from the addition of the station being controlled. We also explicitly examine the impacts of the proximity of the stations and the Spring Festival on theft. The results suggest the following: (1) theft around the new subway stations significantly increases after the stations’ implementation, while the control area does not see much change in thefts; (2) proximity between the neighboring stations’ increases thefts; and (3) theft near the new stations significantly decreases during the month of the Spring Festival. This study contributes to the literature on the relationship between the subway system and crime, especially from a Chinese perspective. The finding of the research can bring insights to urban transit planning, allocation of the police force, and crime prevention.

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