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1. CityLines: Designing Hybrid Hub-and-Spoke Transit System with Urban Big Data

   https://doi.org/10.1109/TBDATA.2018.2840222  

   Li, Yanhua; Liu, Guanxiong; Zhang, Zhi-Li; Luo, Jun; Zhang, Fan (May 2018, IEEE Transactions on Big Data)

   Rapid urbanization has posed significant burden on urban transportation infrastructures. In today's cities, both private and public transits have clear limitations to fulfill passengers' needs for quality of experience (QoE): Public transits operate along fixed routes with long wait time and total transit time; Private transits, such as taxis, private shuttles and ride-hailing services, provide point-to-point transits with high trip fare. In this paper, we propose CityLines, a transformative urban transit system, employing hybrid hub-and-spoke transit model with shared shuttles. Analogous to Airlines services, the proposed CityLines system routes urban trips among spokes through a few hubs or direct paths, with travel time as short as private transits and fare as low as public transits. CityLines allows both point-to-point connection to improve the passenger QoE, and hub-and-spoke connection to reduce the system operation cost. To evaluate the performance of CityLines, we conduct extensive data-driven experiments using one-month real-world trip demand data (from taxis, buses and subway trains) collected from Shenzhen, China. The results demonstrate that CityLines reduces 12.5%-44% average travel time, and aggregates 8.5%-32.6% more trips with ride-sharing over other implementation baselines. 

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2. Human-Centric Urban Transit Evaluation and Planning

   https://doi.org/10.1109/ICDM.2018.00070  

   Wu, Guojun; Li, Yanhua; Bao, Jie; Zheng, Yu; Ye, Jieping; Luo, Jun (November 2018, 2018 IEEE International Conference on Data Mining (ICDM))

   Public transits, such as buses and subway lines, offer affordable ride-sharing services and reduce the road network traffic, thus have significant impacts in mitigating the urban traffic congestion problem. However, it is non-trivial to evaluate a new transit plan, such as a new bus route or a new subway line, of its future ridership prior to actual deployment, since the travel preferences of passengers along the planned routes may vary. In this paper, we make the first attempt to model passengers' preferences of making various transit choices using a Markov Decision Process (MDP). Moreover, we develop a novel inverse preference learning algorithm to infer the passengers' preferences and predict the future human behavior changes, e.g., ridership, of a new urban transit plan before its deployment. We validate our proposed framework using a unique real-world dataset (from Shenzhen, China) with three subway lines opened during the data time span. With the data collected from both before and after the transit plan deployments, Our evaluation results demonstrated that the proposed framework can predict the ridership with only 19.8% relative error, which is 23%-51% lower than other baseline approaches. 

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3. The University of Michigan Implements a Hub-and-Spoke Design to Accommodate Social Distancing in the Campus Bus System Under COVID-19 Restrictions

   https://doi.org/10.1287/inte.2022.1131  

   Chen, Gongyu; Fei, Xinyu; Jia, Huiwen; Yu, Xian; Shen, Siqian (November 2022, INFORMS Journal on Applied Analytics)

   The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools and communities during the pandemic, requiring policy makers to ensure both safety and operational feasibility. In this paper, we develop mixed-integer programming models and simulation tools to redesign routes and bus schedules for operating a real university campus bus system during the COVID-19 pandemic. We propose a hub-and-spoke design and utilize real data of student activities to identify hub locations and bus stops to be used in the new routes. To reduce disease transmission via expiratory aerosol, we design new bus routes that are shorter than 15 minutes to travel and operate using at most 50% seat capacity and the same number of buses before the pandemic. We sample a variety of scenarios that cover variations of peak demand, social distancing requirements, and bus breakdowns to demonstrate the system resiliency of the new routes and schedules via simulation. The new bus routes were implemented and used during the academic year 2020–2021 to ensure social distancing and short travel time. Our approach can be generalized to redesign public transit systems with a social distancing requirement to reduce passengers’ infection risk. History: This paper was refereed. This article has been selected for inclusion in the Special Issue on Analytics Remedies to COVID-19. Funding: This work was supported by the National Science Foundation [Grant CMMI-2041745] and the University of Michigan, College of Engineering. 

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4. MAC: Measuring the Impacts of Anomalies on Travel Time of Multiple Transportation Systems

   https://doi.org/10.1145/3328913  

   Fang, Zhihan; Yang, Yu; Wang, Shuai; Fu, Boyang; Song, Zixing; Zhang, Fan; Zhang, Desheng (June 2019, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Urban anomalies have a large impact on passengers' travel behavior and city infrastructures, which can cause uncertainty on travel time estimation. Understanding the impact of urban anomalies on travel time is of great value for various applications such as urban planning, human mobility studies and navigation systems. Most existing studies on travel time have been focused on the total riding time between two locations on an individual transportation modality. However, passengers often take different modes of transportation, e.g., taxis, subways, buses or private vehicles, and a significant portion of the travel time is spent in the uncertain waiting. In this paper, we study the fine-grained travel time patterns in multiple transportation systems under the impact of urban anomalies. Specifically, (i) we investigate implicit components, including waiting and riding time, in multiple transportation systems; (ii) we measure the impact of real-world anomalies on travel time components; (iii) we design a learning-based model for travel time component prediction with anomalies. Different from existing studies, we implement and evaluate our measurement framework on multiple data sources including four city-scale transportation systems, which are (i) a 14-thousand taxicab network, (ii) a 13-thousand bus network, (iii) a 10-thousand private vehicle network, and (iv) an automatic fare collection system for a public transit network (i.e., subway and bus) with 5 million smart cards. 

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5. Towards demand-oriented flexible rerouting of public transit under uncertainty

   https://doi.org/10.1145/3313237.3313302  

   Nannapaneni, Saideep; Dubey, Abhishek (January 2019, Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering)

   This paper proposes a flexible rerouting strategy for the public transit to accommodate the spatio-temporal variation in the travel demand. Transit routes are typically static in nature, i.e., the buses serve well-defined routes; this results in people living in away from the bus routes choose alternate transit modes such as private automotive vehicles resulting in ever-increasing traffic congestion. In the flex-transit mode, we reroute the buses to accommodate high travel demand areas away from the static routes considering its spatio-temporal variation. We perform clustering to identify several flex stops; these are stops not on the static routes, but with high travel demand around them. We divide the bus stops on the static routes into critical and non-critical bus stops; critical bus stops refer to transfer points, where people change bus routes to reach their destinations. In the existing static scheduling process, some slack time is provided at the end of each trip to account for any travel delays. Thus, the additional travel time incurred due to taking flexible routes is constrained to be less than the available slack time. We use the percent increase in travel demand to analyze the effectiveness of the rerouting process. The proposed methodology is demonstrated using real-world travel data for Route 7 operated by the Nashville Metropolitan Transit Authority (MTA). 

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