Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility
Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the persp...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
University of Minnesota
2020-10-01
|
Series: | Journal of Transport and Land Use |
Subjects: | |
Online Access: | https://www.jtlu.org/index.php/jtlu/article/view/1692 |
id |
doaj-743df440b0564c3fbc03fde506fd70fe |
---|---|
record_format |
Article |
spelling |
doaj-743df440b0564c3fbc03fde506fd70fe2021-08-31T04:36:22ZengUniversity of MinnesotaJournal of Transport and Land Use1938-78492020-10-0113110.5198/jtlu.2020.1692Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibilityShaopeng Zhong0Rong ChengXufeng LiZhong WangYu Jiang1Dalian University of TechnologyTechnical University of Denmark Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors’ knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone. https://www.jtlu.org/index.php/jtlu/article/view/1692Shared autonomous vehicles; road congestion pricing; accessibility; land use and transportation integrated model; data mining |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shaopeng Zhong Rong Cheng Xufeng Li Zhong Wang Yu Jiang |
spellingShingle |
Shaopeng Zhong Rong Cheng Xufeng Li Zhong Wang Yu Jiang Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility Journal of Transport and Land Use Shared autonomous vehicles; road congestion pricing; accessibility; land use and transportation integrated model; data mining |
author_facet |
Shaopeng Zhong Rong Cheng Xufeng Li Zhong Wang Yu Jiang |
author_sort |
Shaopeng Zhong |
title |
Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility |
title_short |
Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility |
title_full |
Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility |
title_fullStr |
Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility |
title_full_unstemmed |
Identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility |
title_sort |
identifying the combined effect of shared autonomous vehicles and congestion pricing on regional job accessibility |
publisher |
University of Minnesota |
series |
Journal of Transport and Land Use |
issn |
1938-7849 |
publishDate |
2020-10-01 |
description |
Most of the existing research on shared autonomous vehicles (SAVs) and road congestion pricing have studied the short-term impact on traffic flow. These types of studies focused on the influences on mobility and ignored the long-term impacts on regional job accessibility. Given this, from the perspective of land use and transportation integration, this study explored the long-term effects of SAVs and cordon-based congestion pricing on regional land use, transportation, and job accessibility. The contributions of this study have been summarized by the following three purposes. First, to the best of the authors’ knowledge, this study was the first attempt to identify the long-term impact of the combination of these two technologies on regional job accessibility. Second, compared to the previous research methodology, this study adopted the land use and transportation integrated model (TRANUS model) and scenario planning to ensure the comprehensiveness and validity of the results. Third, this study analyzed the spatial heterogeneity of the impact of the combination of the two technologies on regional job accessibility in different areas with different built-environment attributes. To realize this purpose, this study quantitatively classified traffic analysis zones (TAZs) using data mining technology, i.e., factor analysis and clustering analysis. Results showed that the introduction of SAVs will contribute to job and population development in the charging zone and reduce the negative effect of road congestion pricing. From the perspective of reducing the average travel time between TAZs, the best strategy will be to implement SAVs alone, followed by integrated use of SAVs and road congestion pricing, while the worst strategy will be to implement the cordon-based congestion pricing policy alone. By comparison, from the perspective of improving regional job accessibility, the effect of introducing SAVs was better than that of road congestion pricing, while the combination of these two technologies was not superior to implementing SAVs alone.
|
topic |
Shared autonomous vehicles; road congestion pricing; accessibility; land use and transportation integrated model; data mining |
url |
https://www.jtlu.org/index.php/jtlu/article/view/1692 |
work_keys_str_mv |
AT shaopengzhong identifyingthecombinedeffectofsharedautonomousvehiclesandcongestionpricingonregionaljobaccessibility AT rongcheng identifyingthecombinedeffectofsharedautonomousvehiclesandcongestionpricingonregionaljobaccessibility AT xufengli identifyingthecombinedeffectofsharedautonomousvehiclesandcongestionpricingonregionaljobaccessibility AT zhongwang identifyingthecombinedeffectofsharedautonomousvehiclesandcongestionpricingonregionaljobaccessibility AT yujiang identifyingthecombinedeffectofsharedautonomousvehiclesandcongestionpricingonregionaljobaccessibility |
_version_ |
1721184419571564544 |