Abstract:Highlights
Establish an adjusted design framework for optimal paths for public transport users considering their preferences at the requested time of travel.
Devise a novel lexicographical comparison methodology with a just noticeable difference (JND) consideration that captures human perception elements combined with preferences over different PT attributes.
Establish the theorem for the comparison method to satisfy the axiom of transitivity and develop a… Show more
“…PT route preferences also depend on other attributes including quality of service, fares, accessibility, connectivity and journey distances 40 . Travelers strive for personalized mobility 22 , 41 with the option of changing the relative value of TBC and CBT attributes at any time during the day. PT will prevail over PC as a conscious decision if the public will consider it a better fit for its personalized mobility needs.…”
Section: Transit Vs Private Carsmentioning
confidence: 99%
“…Sustainability necessarily involves the capability to stand constantly, and therefore, we ought to consider people's preferences and comfort, on top of understanding our future layout through an integrated perspective 20 of environment, society, and economics. This notion is an element of the recommendation in the discussion section to take all four components—globalization 21 , personalization 22 , prioritization 23 and coordination of standardization 24 —into account to ultimately attain seamless and appealing urban mobility 4 , 25 .…”
Unforeseeable developments will accompany progressive COVID-19 recovery globally. Similarly, science will inform changes amidst its own progress. Social isolation and distancing imposed by the pandemic are likely to result in changed habits, behavior, and thinking paradigms. Inevitably, this should affect the tremendous confusion inhibiting automated urban mobility's evolution. While mobility often seems magnanimously resistant to change, using international data, this analysis shows road traffic, the largest net contributor to global warming, is responsible for even greater damages. The core claim justifies replacing private cars (PCs) by existing and future public transit (PT) vehicles. In testing 17 major cities globally, 94% of the scenarios proved PT superior or equivalent to PCs for reducing travel time. As a result, a foreseeable, future scenario shows potential reduction in car traffic by approximately two-thirds compared with the current situation. In two arenas, proactive government can promote such sustainable urban mobility: (1) developing autonomous vehicles for PT only; (2) coordinating standardization for seamless urban mobility. These global decisions for improving our lives in the future are likely to be better received and understood subsequent to COVID-19, as the focus of our concerns changes from what preoccupied us under the circumstances prior to the pandemic.
“…PT route preferences also depend on other attributes including quality of service, fares, accessibility, connectivity and journey distances 40 . Travelers strive for personalized mobility 22 , 41 with the option of changing the relative value of TBC and CBT attributes at any time during the day. PT will prevail over PC as a conscious decision if the public will consider it a better fit for its personalized mobility needs.…”
Section: Transit Vs Private Carsmentioning
confidence: 99%
“…Sustainability necessarily involves the capability to stand constantly, and therefore, we ought to consider people's preferences and comfort, on top of understanding our future layout through an integrated perspective 20 of environment, society, and economics. This notion is an element of the recommendation in the discussion section to take all four components—globalization 21 , personalization 22 , prioritization 23 and coordination of standardization 24 —into account to ultimately attain seamless and appealing urban mobility 4 , 25 .…”
Unforeseeable developments will accompany progressive COVID-19 recovery globally. Similarly, science will inform changes amidst its own progress. Social isolation and distancing imposed by the pandemic are likely to result in changed habits, behavior, and thinking paradigms. Inevitably, this should affect the tremendous confusion inhibiting automated urban mobility's evolution. While mobility often seems magnanimously resistant to change, using international data, this analysis shows road traffic, the largest net contributor to global warming, is responsible for even greater damages. The core claim justifies replacing private cars (PCs) by existing and future public transit (PT) vehicles. In testing 17 major cities globally, 94% of the scenarios proved PT superior or equivalent to PCs for reducing travel time. As a result, a foreseeable, future scenario shows potential reduction in car traffic by approximately two-thirds compared with the current situation. In two arenas, proactive government can promote such sustainable urban mobility: (1) developing autonomous vehicles for PT only; (2) coordinating standardization for seamless urban mobility. These global decisions for improving our lives in the future are likely to be better received and understood subsequent to COVID-19, as the focus of our concerns changes from what preoccupied us under the circumstances prior to the pandemic.
“…Also, the link (China-province) shows these researchers focused their search on the provinces where the break out is traced such as Wuhan and Macao ( Lio et al, 2020 ). The heavy node travel has heavy links with travel-high-risk, travel-history signifying that some researchers tried to understand the pandemic by studying passengers' travel history and their prominent risks ( Ceder & Jiang 2020 ; Shi et al, 2020 ). Their degree centrality measures values also explain the linkages of the mentioned nodes, keyword travel (21) is leading followed by China (20), epidemic (20), travelers (17) and case (17 Fig.…”
“…In traditional practice, metro operators empirically recommend the route with the shortest travel time or the lowest travel cost to passengers. Nevertheless, existing studies have shown that passengers with different travel purposes pay different attention to different factors [47,48]. For example, commuters may be more concerned about travel time reliability.…”
Section: Applications Based On Passenger Mobility Patternsmentioning
Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.
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