Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Predicting passenger loading level on a train car: A Bayesian approach (1808.06962v1)

Published 1 Aug 2018 in eess.SP and cs.SY

Abstract: Crowding in train cars is increasingly a major concern for transit agencies. From the perspective of the passengers and the transit agencies, overcrowding of the train cars has several negative consequences such as: (i) extended duration of passengers boarding and alighting which leads to longer dwell times, (ii) subsequent disruption of the headway and the schedule, and (iii) passenger dissatisfaction (e.g. increased stress and lack of privacy). Moreover, overcrowding during peak service hours also indicates inadequate infrastructure to meet the passenger demands. Realizing the importance of the crowding issue, transit agencies have developed measures to assess the crowding levels. The Transit Capacity and Quality of Service Manual provides guidelines on thresholds for crowding in transit systems in the United States.

Citations (1)

Summary

We haven't generated a summary for this paper yet.