Intercity travel management and optimization based on the intervention of government and enterprise

2021 ◽  
Author(s):  
XiaoShi Wang
Keyword(s):  
Intercity Travel

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

Transferability of aggregate intercity total demand models

1995 ◽  
Vol 22 (2) ◽  
pp. 283-291
Author(s):  
Amal S. Kumarage ◽  
S. C. Wirasinghe
Keyword(s):  
Sri Lanka ◽  
Public Transportation ◽  
Travel Demand ◽  
Contextual Factors ◽  
Demand Model ◽  
Aggregate Model ◽  
Demand Models ◽  
Intercity Travel ◽  
Model Transfer ◽  
Contextual Characteristics

Over the last 15 years, extensive research has been done on the transferability of travel demand models. However, much of this work has been concentrated towards investigating the transferability of disaggregate mode choice models. The transferability of an aggregate total demand model for intercity travel is examined. Model transfer is possible only when a number of preconditions for transferability are satisfied. One of the principal obstacles to the successful transfer of intercity demand models is the inability to overcome the contextual differences between calibration and application. Here, the components of the intercity total demand model are separated into exogenous and intrinsic (contextual) factors. The latter is thereafter classified as being either transferable or nontransferable. It is shown that transferable attributes can accompany a model during transfer. Nontransferable attributes, on the other hand, will free the model of city or city-pair specific contextual characteristics which should not be transferred to other city pairs. The issues involved in transferring an aggregate model are also investigated. Aggregate data on interdistrict travel by public transportation in Sri Lanka have been used to successfully calibrate a total demand model with a number of transferable and nontransferable attributes that represent both temporal and spatial contextual factors. It is shown that the forecasting ability of this model is far superior to a counterpart model without the intrinsic variables. Key words: travel demand, aggregate, forecasting, transferability, intercity, Sri Lanka.

Development of disaggregate mode choice models of intercity travel in Canada

1992 ◽  
Vol 19 (6) ◽  
pp. 965-974 ◽  
Author(s):  
Walid M. Abdelwahab ◽  
J. David Innes ◽  
Albert M. Stevens
Keyword(s):  
Travel Behavior ◽  
Mode Choice ◽  
Geographical Location ◽  
Predictive Ability ◽  
Multinomial Logit ◽  
Choice Models ◽  
Trip Purpose ◽  
Market Shares ◽  
Intercity Travel ◽  
Statistics Canada

This paper reports and discusses the results of an effort to develop disaggregate behavioral mode choice models of intercity travel in Canada. Currently available data bases of intercity travel in Canada are reviewed. The feasibility of using data from national travel surveys to develop statistically reliable intercity mode choice models is examined, and directions for future disaggregate data collection efforts are offered. The models developed are of the multinomial logit (MNL) type which included all intercity passenger travel modes: auto, air, bus, and rail. For purposes of estimation, the travel market was segmented by trip length (short, long); trip purpose (business, recreational); and geographical location of the trip (east, west). Then, a separate model was estimated in each sector. The models were estimated using the data collected by Statistics Canada as a part of the Labor Force Survey (The Canadian Travel Survey, CTS). The quality of the calibrated models varied from one region to another and from one travel sector to another. Overall, the models were reasonably accurate in predicting modal shares of the most frequently used modes (auto and air). The underrepresentation of the bus and rail modes in the data sets led to a deterioration in the performance of the models in predicting market shares of these two modes. More specifically, the predictive ability of the models measured by the likelihood ratio index varied from a low of 0.58 in the short travel sector to a high of 0.94 in the long travel sector. The transferability of the models described in this study was recently examined by Abdelwahab (1991). Key words: mode choice, disaggregate, travel behavior, multinomial logit, intercity, data base.

scholarly journals Influence of Weather Conditions on the Intercity Travel Mode Choice: A Case of Xi’an

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xiaowei Li ◽  
Qiangqiang Ma ◽  
Wenbo Wang ◽  
Baojie Wang
Keyword(s):  
High Speed ◽  
Mode Choice ◽  
Mode Selection ◽  
Weather Conditions ◽  
Individual Characteristics ◽  
Activity Data ◽  
Travel Mode ◽  
Intercity Travel ◽  
Travel Mode Choice ◽  
Weather Characteristics

To explore the influence of weather conditions on the choice of the intercity travel mode of travelers, four modes of traveler transportation were studied in Xi'an, China, in March 2019: airplane, high-speed rail, conventional train, and express bus. The individual characteristics of travelers and intercity travel activity data were obtained, and they were matched with the weather characteristics at the departure time of the travelers. The Bayesian multinomial logit regression was employed to explore the relationship between the travel mode choice and weather characteristics. The results showed that temperature, relative humidity, rainfall, wind, air quality index, and visibility had significant effects on the travel mode selection of travelers, and the addition of these variables could improve the model’s predictive performance. The research results can provide a scientific decision basis for traveler flow transfer and the prediction of traffic modes choice due to the effects of climate change.

scholarly journals Modeling Intercity Travel Mode Choice with Data Balance Changes: A Comparative Analysis of Bayesian Logit Model and Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Xiaowei Li ◽  
Yuting Wang ◽  
Yao Wu ◽  
Jun Chen ◽  
Jibiao Zhou
Keyword(s):  
Comparative Analysis ◽  
High Speed ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Multinomial Logit ◽  
Neural Network Models ◽  
Activity Data ◽  
Travel Mode ◽  
Intercity Travel ◽  
Artificial Neural

This study conducts a comprehensive comparative analysis of regression-based multinomial models and artificial neural network models in intercity travel mode choices. The four intercity travel modes of airplane, high-speed rail (HSR), train, and express bus were used for analysis. Passengers’ activity data over the process of intercity travel were collected to develop the models. The standard multinomial logit (MNL) regression and Bayesian multinomial logit (BMNL) regression were compared with the radial basis function (RBF) and multilayer perceptron (MLP). The results show that MLP performs best in terms of predictive accuracy, followed by BMNL and MNL, and RBF is the least accurate. The performances of all models were examined against changes in data balance, and it was found that rebalancing can improve fitting performance while slightly reducing the predictive performance. This comparative study and its parameter estimation shed new light on the comparison of traditional and emerging models in travel behavior studies, and the findings can be used as heuristic guidance for all stakeholders.

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