Spatiotemporal clustering in urban transportation

Author:

Fei Xiqi1,Gkountouna Olga1

Affiliation:

1. George Mason University

Abstract

Public buses are an important part of the urban transportation mix. However, a considerable disadvantage of buses is their slow speed, which is in part due to frequent stops, but also due to the lack of segregation from other vehicles in traffic. As such, assessing bus routes and the respective sections that are prone to congestion is an important aspect of route planning, scheduling, and the creation of dedicated bus lanes. In this work we use bus tracking data from the Washington Metropolitan Area Transit Authority, to discover speed patterns of specific bus routes in relation to the road network throughout the day. Specifically, we focus on using these patterns to identify free flow segments, bus stop locations, traffic light locations and road segments prone to congestion.

Publisher

Association for Computing Machinery (ACM)

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Multimodal Sensing for Predicting Real-time Biking Behavior based on Contextual Information;2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops);2024-03-11

2. Exploring bus tracking data to characterize urban traffic congestion;Journal of Urban Mobility;2023-12

3. Exploiting Multi-modal Contextual Sensing for City-bus’s Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction;ACM Transactions on Internet of Things;2023-02-23

4. Visualizing public transit system operation with GTFS data: A case study of Calgary, Canada;Heliyon;2020-04

5. People Flow Analysis Based on Anonymous OD Trip Data;Communications in Computer and Information Science;2020

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