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1. Train to Vehicle: Toward Sustainable Transportation in Dense Urban Regions

   https://doi.org/10.3390/smartcities6050127  

   Mohamed, Ahmed Ali; Ahmad, Rohama; Singh, Jaskaran; Rahman, Ahmed S. (October 2023, Smart Cities)

   This article investigates the feasibility of using regenerative energy from braking trains to charge electric buses in the context of New York City’s (NYC) subway and electric bus networks. A case study centered around NYC’s system has been performed to evaluate the benefits and challenges pertaining to the use of the preexisting subway network as a power supply for its new all-electric buses. The analysis shows that charging electric buses via the subway system during subway off-peak periods does not hinder regular train operation. In addition, having the charging electric buses connected to the third rail allows for more regenerative braking energy (RBE) to be recuperated, decreasing the energy wasted throughout the system. It was also found that including a wayside energy storage system (WESS) reduces the overall substation peak power consumption. 

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2. Dynamic Integration of Heterogeneous Transportation Modes Under Disruptive Events

   Yuan, Y.; Zhang, D.; Miao, F.; Stankovic, J.; He, T.; Pappas, G.; Lin, S. (January 2018, ACM/IEEE International Conference on Cyber-Physical Systems)

   An integrated urban transportation system usually consists of multiple transport modes that have complementary characteristics of capacities, speeds, and costs, facilitating smooth passenger transfers according to planned schedules. However, such an integration is not designed to operate under disruptive events, e.g., a signal failure at a subway station or a breakdown of a bus, which have rippling effects on passenger demand and significantly increase delays. To address these disruptive events, current solutions mainly rely on a substitute service to transport passengers from and to affected areas using adhoc schedules. To fully utilize heterogeneous transportation systems under disruptive events, we design a service called eRoute based on a hierarchical receding horizon control framework to automatically reroute, reschedule, and reallocate multi-mode transportation systems based on real-time and predicted demand and supply. Focusing on an integration of subway and bus, we implement and evaluate eRoute with large datasets including (i) a bus system with 13,000 buses, (ii) a subway system with 127 subway stations, (iii) an automatic fare collection system with a total of 16,840 readers and 8 million card users from a metropolitan city. The data-driven evaluation results show that our solution improves the ratio of served passengers (RSP) by up to 11.5 times and reduces the average traveling time by up to 82.1% compared with existing solutions. 

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3. 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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4. A long-term travel delay measurement study based on multi-modal human mobility data

   https://doi.org/10.1038/s41598-022-19394-z  

   Fang, Zhihan; Wang, Guang; Yang, Yu; Zhang, Fan; Wang, Yang; Zhang, Desheng (September 2022, Scientific Reports)

   Abstract Understanding human mobility is of great significance for sustainable transportation planning. Long-term travel delay change is a key metric to measure human mobility evolution in cities. However, it is challenging to quantify the long-term travel delay because it happens in different modalities, e.g., subway, taxi, bus, and personal cars, with implicated coupling. More importantly, the data for long-term multi-modal delay modeling is challenging to obtain in practice. As a result, the existing travel delay measurements mainly focus on either single-modal system or short-term mobility patterns, which cannot reveal the long-term travel dynamics and the impact among multi-modal systems. In this paper, we perform a travel delay measurement study to quantify and understand long-term multi-modal travel delay. Our measurement study utilizes a 5-year dataset of 8 million residents from 2013 to 2017 including a subway system with 3 million daily passengers, a 15 thousand taxi system, a 10 thousand personal car system, and a 13 thousand bus system in the Chinese city Shenzhen. We share new observations as follows: (1) the aboveground system has a higher delay increase overall than that of the underground system but the increase of it is slow down; (2) the underground system infrastructure upgrades decreases the aboveground system travel delay increase in contrast to the increase the underground system travel delay caused by the aboveground system infrastructure upgrades; (3) the travel delays of the underground system decreases in the higher population region and during the peak hours. 

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5. Rezoning Educational Opportunities: The Relationship Between School Attendance Zone Boundary Changes and Access to Schools of Varying Quality

   https://doi.org/10.1177/08959048241271370  

   Asson, Sarah (November 2024, Educational Policy)

   U.S. public schools provide substantially different educational opportunities to students—even within school districts, where attendance zone boundaries (AZBs) shape most children’s access to schools. The (re)drawing of AZBs is therefore a highly consequential policy decision. In this paper, I study how AZB changes in the Washington, D.C. metropolitan area affect children of different races between 2000 and 2020, including how they alter children’s travel times to school and access to school-level educational opportunities. Findings are starkly unequal, with rezoning disproportionately negatively affecting Black and Hispanic children. Results highlight the need for educational leaders to explicitly consider how AZB changes will redistribute opportunity within their districts. 

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