Public Transport最新文献_第7页

An optimization model for planning limited-stop transit operations 一种限停交通运营规划优化模型

IF 2.6

Public Transport Pub Date : 2022-03-01 DOI: 10.1007/s12469-022-00293-5

Mahmood Mahmoodi Nesheli, S. Srikukenthiran, A. Shalaby

引用次数: 3

Resolution of coupling order and station level constraints in train unit scheduling 列车单元调度中耦合顺序和站级约束的求解

IF 2.6

Public Transport Pub Date : 2022-03-01 DOI: 10.1007/s12469-022-00295-3

L. Lei, R. S. Kwan, Zhiyuan Lin, P. Copado-Mendez

引用次数: 0

Revisiting the richness of integrated vehicle and crew scheduling 重新审视综合车辆和人员调度的丰富性

IF 2.6

Public Transport Pub Date : 2022-02-23 DOI: 10.1007/s12469-022-00292-6

Liping Ge, N. Kliewer, A. Nourmohammadzadeh, Stefan Voß, Lin Xie

引用次数: 6

Measurement and classification of transit delays using GTFS-RT data 利用GTFS-RT数据测量和分类运输延误

IF 2.6

Public Transport Pub Date : 2022-02-23 DOI: 10.1007/s12469-022-00291-7

Zack Aemmer, A. Ranjbari, D. MacKenzie

引用次数: 7

Decoding customer experiences in rail transport service: application of hybrid sentiment analysis 铁路运输服务客户体验解码:混合情感分析的应用

IF 2.6

Public Transport Pub Date : 2022-02-22 DOI: 10.1007/s12469-021-00289-7

Dibya Nandan Mishra, R. Panda

引用次数: 5

Crime and safety in transit environments: a systematic review of the English and the French literature, 1970–2020 交通环境中的犯罪和安全:1970-2020年英法文献的系统回顾

IF 2.6

Public Transport Pub Date : 2022-01-31 DOI: 10.1007/s12469-021-00265-1

V. Ceccato, Nathan Gaudelet, Gabin Graf

引用次数: 1

Robustness and disturbances in public transport. 公共交通的稳健性和干扰。

IF 2.6

Public Transport Pub Date : 2022-01-01 Epub Date: 2022-06-04 DOI: 10.1007/s12469-022-00301-8

Liping Ge, Stefan Voß, Lin Xie

引用次数: 0

Abstracting mobility flows from bike-sharing systems. 从共享单车系统中抽象出流动流。

IF 2.6

Public Transport Pub Date : 2022-01-01 Epub Date: 2021-03-16 DOI: 10.1007/s12469-020-00259-5

Fabio Kon, Éderson Cássio Ferreira, Higor Amario de Souza, Fábio Duarte, Paolo Santi, Carlo Ratti

{"title":"Abstracting mobility flows from bike-sharing systems.","authors":"Fabio Kon, Éderson Cássio Ferreira, Higor Amario de Souza, Fábio Duarte, Paolo Santi, Carlo Ratti","doi":"10.1007/s12469-020-00259-5","DOIUrl":"10.1007/s12469-020-00259-5","url":null,"abstract":"Bicycling has grown significantly in the past ten years. In some regions, the implementation of large-scale bike-sharing systems and improved cycling infrastructure are two of the factors enabling this growth. An increase in non-motorized modes of transportation makes our cities more human, decreases pollution, traffic, and improves quality of life. In many cities around the world, urban planners and policymakers are looking at cycling as a sustainable way of improving urban mobility. Although bike-sharing systems generate abundant data about their users' travel habits, most cities still rely on traditional tools and methods for planning and policy-making. Recent technological advances enable the collection and analysis of large amounts of data about urban mobility, which can serve as a solid basis for evidence-based policy-making. In this paper, we introduce a novel analytical method that can be used to process millions of bike-sharing trips and analyze bike-sharing mobility, abstracting relevant mobility flows across specific urban areas. Backed by a visualization platform, this method provides a comprehensive set of analytical tools to support public authorities in making data-driven policy and planning decisions. This paper illustrates the use of the method with a case study of the Greater Boston bike-sharing system and, as a result, presents new findings about that particular system. Finally, an assessment with expert users showed that this method and tool were considered very useful, relatively easy to use and that they intend to adopt the tool in the near future.","PeriodicalId":46539,"journal":{"name":"Public Transport","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73794686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing public transport in the city of Buenos Aires with MobilityDB. 利用 MobilityDB 分析布宜诺斯艾利斯市的公共交通。

IF 2.6

Public Transport Pub Date : 2022-01-01 Epub Date: 2022-03-01 DOI: 10.1007/s12469-022-00290-8

Juan Godfrid, Pablo Radnic, Alejandro Vaisman, Esteban Zimányi

引用次数: 0

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data. 利用台北捷运智能卡数据研究 COVID-19 影响下的时空移动模式和捷运使用率变化。

IF 2.3

Public Transport Pub Date : 2022-01-01 Epub Date: 2021-08-16 DOI: 10.1007/s12469-021-00280-2

Christian Martin Mützel, Joachim Scheiner

{"title":"Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data.","authors":"Christian Martin Mützel, Joachim Scheiner","doi":"10.1007/s12469-021-00280-2","DOIUrl":"10.1007/s12469-021-00280-2","url":null,"abstract":"Modern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.","PeriodicalId":46539,"journal":{"name":"Public Transport","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84641401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0