scholarly journals A Data-Based Bi-Objective Approach to Explore the Accessibility of Multimodal Public Transport Networks

2021 ◽  
Vol 10 (11) ◽  
pp. 758
Author(s):  
Wentao Yu ◽  
Huijun Sun ◽  
Tao Feng ◽  
Jianjun Wu ◽  
Ying Lv ◽  
...  
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Traffic Management ◽  
Large Scale ◽  
Transport Networks ◽  
Time Uncertainty ◽  
Smart Card Data ◽  
Research Gap ◽  
Travel Time Uncertainty

One of the top concerns for travelers when choosing public transportation is whether they can reach their destination in limited time and monetary cost on the basis of ensured reliability. However, the existing literature shows no studies on how to evaluate bi-objective multimodal accessibility under travel time uncertainty. In order to fill this research gap, this paper creates a multimodal super network based on smart card data in which the transfers among taxi, bus, and subway modes are developed and applied. Next, we propose a two-stage opportunity accessibility model to calculate bi-objective multimodal accessibility under travel time uncertainty. Then we propose a multimodal reliability path finding model and a reliability boundary convergence algorithm to solve this problem. Finally, we conduct a large-scale real-world case study. It is found that the impedance heterogeneity between different modes is significant, and multimodal travel has better accessibility than a unimodal one. Although multimodal accessibility decreases as the reliability increases, the advantage of multimodal over unimodal accessibility increases with reliability, and it can be improved up to 14.61% by multimodal transfers. This model can effectively guide traffic management departments to improve traffic accessibility in terms of time and cost and advise commuters to choose living places.

Multiclass, Multicriteria Dynamic Traffic Assignment with Path-Dependent Link Cost and Entropy-Based Risk Preference

2017 ◽  
Vol 2667 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Jiangbo Gabriel Yu ◽  
R. Jayakrishnan
Keyword(s):  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Time Uncertainty ◽  
Link Cost ◽  
Travel Time Uncertainty ◽  
Path Dependent ◽  
Dependent Link

Challenges arise in dynamic traffic assignment (DTA) when heterogeneous users evaluate choices on the basis of multiple interrelated criteria such as travel time and travel time uncertainty. This paper proposes a density-based formulation along with a stochastic quasigradient projection (SQGP) solution scheme with the aid of a traffic simulator. Path-dependent link cost is proposed to allow for the objective function formulation and more tractable analysis. The criteria in the discussion and the case study are travel time (link-additive), monetary cost (non additive), and travel time uncertainty (path-dependent link-additive). An information entropy-based uncertainty measure is proposed because of concerns about using conventional measures such as variability and reliability. The case study shows stochastic and efficient convergence, demonstrates the ability of SQGP to bypass local optima, and exemplifies the significant effect of using path-independent and path-dependent link costs to forecast traffic pattern and toll revenue. The results also suggest that a pricing strategy aimed at optimizing travel time and reliability for different user classes should consider travel time correlations between toll segments and the adjacent no-toll segments if enumerating paths is practically infeasible.

scholarly journals Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Vol 178 (2) ◽  
pp. 313-339
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Random Effects ◽  
Mean Squared Error ◽  
Random Effects Model ◽  
Location Error ◽  
Crust And Upper Mantle ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Event Location ◽  
Travel Time Uncertainty

AbstractThe regional seismic travel time (RSTT) model and software were developed to improve travel-time prediction accuracy by accounting for three-dimensional crust and upper mantle structure. Travel-time uncertainty estimates are used in the process of associating seismic phases to events and to accurately calculate location uncertainty bounds (i.e. event location error ellipses). We improve on the current distance-dependent uncertainty parameterization for RSTT using a random effects model to estimate slowness (inverse velocity) uncertainty as a mean squared error for each model parameter. The random effects model separates the error between observed slowness and model predicted slowness into bias and random components. The path-specific travel-time uncertainty is calculated by integrating these mean squared errors along a seismic-phase ray path. We demonstrate that event location error ellipses computed for a 90% coverage ellipse metric (used by the Comprehensive Nuclear-Test-Ban Treaty Organization International Data Centre (IDC)), and using the path-specific travel-time uncertainty approach, are more representative (median 82.5% ellipse percentage) of true location error than error ellipses computed using distance-dependent travel-time uncertainties (median 70.1%). We also demonstrate measurable improvement in location uncertainties using the RSTT method compared to the current station correction approach used at the IDC (median 74.3% coverage ellipse).

Robustifying humanitarian relief systems against travel time uncertainty

2021 ◽  
Author(s):  
Jianghua Zhang ◽  
Yang Liu ◽  
Guodong Yu ◽  
Zuo‐Jun (Max) Shen
Keyword(s):  
Travel Time ◽  
Humanitarian Relief ◽  
Time Uncertainty ◽  
Travel Time Uncertainty

scholarly journals Correction to: Updates to the Regional Seismic Travel Time (RSTT) Model: 2. Path-dependent Travel-time Uncertainty

2021 ◽  
Author(s):  
Michael L. Begnaud ◽  
Dale N. Anderson ◽  
Stephen C. Myers ◽  
Brian Young ◽  
James R. Hipp ◽  
...  
Keyword(s):  
Travel Time ◽  
Time Uncertainty ◽  
Travel Time Uncertainty ◽  
Path Dependent

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

scholarly journals Understanding the integration of buses and metro systems using smart card data

2019 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Diao Lin ◽  
Ruoxin Zhu
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Pearson Correlation ◽  
Metro Station ◽  
Smart Card Data ◽  
Pearson Correlation Test ◽  
Metro Systems ◽  
The City ◽  
Afternoon Peak

<p><strong>Abstract.</strong> Buses are considered as an important type of feeder model for urban metro systems. It is important to understand the integration of buses and metro systems for promoting public transportation. Using smart card data generated by automatic fare collection systems, we aim at exploring the characteristics of bus-and-metro integration. Taking Shanghai as a case study, we first introduced a rule-based method to extract metro trips and bus-and-metro trips from the raw smart card records. Based on the identified trips, we conducted three analyses to explore the characteristics of bus-and-metro integration. The first analysis showed that 46% users have at least two times of using buses to access metro stations during five weekdays. By combining the ridership of metro and bus-and-metro, the second analysis examined how the share of buses as the feeder mode change across space and time. Results showed that the share of buses as the feeder mode in morning peak hours is much larger than in afternoon peak hours, and metro stations away from the city center tend to have a larger share. Pearson correlation test was employed in the third analysis to explore the factors associated with the ratios of bus-and-metro trips. The metro station density and access metro duration are positively associated with the ratios. The number of bus lines around 100&amp;thinsp;m to 400&amp;thinsp;m of metro stations all showed a negative association, and the coefficient for 200&amp;thinsp;m is the largest. In addition, the temporal differences of the coefficients also suggest the importance of a factor might change with respect to different times. These results enhanced our understanding of the integration of buses and metro systems.</p>

Bi-objective autonomous vehicle repositioning problem with travel time uncertainty

4OR ◽  
2020 ◽  
Vol 18 (4) ◽  
pp. 477-505
Author(s):  
Hadi Charkhgard ◽  
Mahdi Takalloo ◽  
Zulqarnain Haider
Keyword(s):  
Travel Time ◽  
Autonomous Vehicle ◽  
Time Uncertainty ◽  
Travel Time Uncertainty
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