Intercity Travel Research Articles

Exploring Influencing Factors of Intercity Mode Choice from View of Entire Travel Chain

Exploring the influencing factors of intercity travel mode choice can reveal passengers’ travel decision mechanisms and help traffic departments to develop an effective demand management policy. To investigate these factors, a survey was conducted in Xi’an, China, to collect data about passengers’ travel chains, including airplane, high-speed railway (HSR), train, and express bus. A Bayesian mixed multinomial logit model is developed to identify significant factors and explicate unobserved heterogeneity across observations. The effect of significant factors on intercity travel mode choice is quantitatively assessed by the odds ratio (OR) technique. The results show that the Bayesian mixed multinomial logit model outperforms the traditional Bayesian multinomial logit model, indicating that accommodating the unobserved heterogeneity across observations can improve the model fit. The model estimation results show that ticket purchasing method, comfort, punctuality, and access time are random parameters that have heterogeneous effects on intercity travel mode choice.

Price-based regulation of oligopolistic markets under discrete choice models of demand

We propose a framework to find optimal price-based policies to regulate markets characterized by oligopolistic competition and in which consumers make a discrete choice among a finite set of alternatives. The framework accommodates general discrete choice models available in the literature in order to capture heterogeneous consumer behavior. In our work, consumers are utility maximizers and are modeled according to random utility theory. Suppliers are modeled as profit maximizers, according to the traditional microeconomic treatment. Market competition is modeled as a non-cooperative game, for which an approximate equilibrium solution is sought. Finally, the regulator can affect the behavior of all other agents by giving subsidies or imposing taxes to consumers. In transport markets, economic instruments might target specific alternatives, to reduce externalities such as congestion or emissions, or specific segments of the population, to achieve social welfare objectives. In public policy, different agents have different individual or social objectives, possibly conflicting, which must be taken into account within a social welfare function. We present a mixed integer optimization model to find optimal policies subject to supplier profit maximization and consumer utility maximization constraints. Then, we propose a model-based heuristic approach based on the fixed-point iteration algorithm that finds an approximate equilibrium solution for the market. Numerical experiments on an intercity travel case study show how the regulator can optimize its decisions under different scenarios.

Effect of COVID-19 pandemic on ambient air quality and excess risk of particulate matter in Turkey

The COVID-19 pandemic, which has reached 4 million global cases as of March 10, 2020, has become a worldwide problem. Turkey is one of the most affected (9th in the world) country with 139 771 cases. An intermittent curfew policy that differ for three age groups, and an intercity travel ban varying within the country have been implemented. The effects of changes in social life and industrial activity in terms of environmental pollution are not yet known. The short-term effects on PM2.5, PM10, SO2, NO2, NO, NOx, O3 and CO concentrations measured at 51 air quality measurement stations (AQMS) in 11 cities in March – April period of 2020 were statistically compared with that of the previous year. While PM2.5 (9/14 AQMS) and PM10 (29/35 AQMS) concentrations were not significantly affected, NO (12/24 AQMS), NO2 (20/29 AQMS), NOX (17/25 AQMS) concentrations were decreased, SO2 concentrations at half of the AQMSs (11/25) did not show a significant change. There were stations at which higher pollutant concentrations were measured in the study period in 2020 compared to that of 2019. Excess risks associated with PM2.5 and PM10 were estimated to be variable, albeit with a small difference. In conclusion, the heterogeneous actions taken in response to the COVID-19 pandemic resulted in mixed effects on ambient air quality.

Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

Intercity travel by residents promotes the regathering and dissemination of social and economic factors. Based on big data from Tencent’s location-based service, 346 cities above the prefecture level in China were chosen as study objects, with 2018 as the study time node. To construct the intercity residents’ travel network, complex network analysis and GIS spatial analysis methods were used. Furthermore, when analyzing the structural characteristics and spatial differences of Chinese residents’ intercity travel from different time perspectives (the whole year, daily, Spring Festival travel rush, and special holidays), Gephi network analysis tools and ArcGIS spatial analysis software were used. The following are the major findings: daily and the whole year intercity travel by Chinese residents, as well as intercity travel during special holidays and the Spring Festival, all exhibit the “diamond” structure, with Beijing, Shanghai, Guangzhou-Shenzhen, and Chengdu-Chongqing at the core. The distribution of lines in and around the “diamond” is large and concentrated from the perspective of the hierarchical nature of the residents’ intercity travel network. Significant increases in high-intensity population flow lines within the “diamond” can be seen during Spring Festival travel and holidays. The number of cities involved in the inflow line is significantly greater than that involved in the outflow line, as demonstrated by the number of residents in the first point of travel, indicating that there is a difference between the central cities flowing into and out of the network. The first flow of the central city is the most visible during the Spring Festival travel period. Most cities in the resident intercity travel network have relatively low degrees of centrality, closeness centrality, and betweenness centrality, and the number of cities with large values of the three is small, and they are concentrated in the apex and interior of the “diamond” structure.

Megaregional Passenger Transportation Hub Location Problem Considering Congestion Effects

The need to make effective plans for locating transportation hubs is of increasing importance in the megaregional area, as recent research suggests that the growing intercity travel demand affects the efficiency of a megaregional transportation system. This paper investigates a hierarchical facility location problem in a megaregional passenger transportation network. The aim of the study is to determine the locations of hub facilities at different hierarchical levels and distribute the demands to these facilities with minimum total cost, including investment, transportation, and congestion costs. The problem is formulated as a mixed-integer nonlinear programming model considering the service availability structure and hub congestion effects. A case study is designed to demonstrate the effectiveness of the proposed model in the Wuhan metropolitan area. The results show that the congestion effects can be addressed by reallocating the demand to balance the hub utilisation or constructing new hubs to increase the network capacity. The methods of appropriately locating hubs and distributing traffic flows are proposed to optimise the megaregional passenger transportation networks, which has important implications for decision makers.

Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel

Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and transfer systems. However, few studies have analyzed the influencing factors of intercity car-hailing travel or forecast travel demand. This paper takes trips between Yinchuan and Shizuishan, China, as the research case and analyzes the influence of time, space, passengers, and the environment on intercity online car-hailing travel. The relationship between the urban built environment and intercity online car-hailing travel demand is also investigated through a geographically weighted regression (GWR) model. We find that the peak hours for intercity car-hailing trips are between 9:00 and 10:00 and between 16:00 and 18:00, which are significantly different from those for intracity trips. Weather conditions strongly affect the mobility of intercity trips. The urban built environment also has a significant impact on intercity car-hailing ridership, and residential districts and transportation facilities are the factors with the greatest influence on intercity online car-hailing travel. These results can provide practical help to city managers improve the management of intercity traffic and develop better transportation policies.

Exploring the dynamic impacts of COVID-19 on intercity travel in China

Many studies have explored the effects of transportation and population movement on the spread of pandemics. However, little attention has been paid to the dynamic impact of pandemics on intercity travel and its recovery during a public health event period. Using intercity mobility and COVID-19 pandemic data, this study adopts the gradient boosting decision tree method to explore the dynamic effects of the COVID-19 on intercity travel in China. The influencing factors were classified into daily time-varying factors and time-invariant factors. The results show that China's intercity travel decreased on average by 51.35% from Jan 26 to Apr 7, 2020. Furtherly, the COVID-19 pandemic reduces intercity travel directly and indirectly by influencing industry development and transport connectivity. With the spread of COVID-19 and changes of control measures, the relationship between intercity travel and COVID-19, socio-economic development, transport is not linear. The relationship between intercity travel and secondary industry is illustrated by an inverted U-shaped curve from pre-pandemic to post-pandemic, whereas that with tertiary industry can be explained by a U-shaped curve. Meanwhile, this study highlights the dynamic effect of the COVID-19 on intercity mobility. These implications shed light on policies regarding the control measures during public health events that should include the dynamic impact of pandemics on intercity travel.

Optimal charging strategy for intercity travels of battery electric vehicles

Limited driving range, long charging time and high charging costs affect the use of battery electric vehicles (BEVs) for intercity travels and often compel drivers to charge their vehicles more than once. This study proposes a multistage optimisation model to provide BEV drivers with a charging strategy for intercity travel. This model aims to jointly minimise travel time and charging cost and to determine the optimal amount of charged energy in each charging station located along the available routes. A dynamic programming method for solving this model is also designed. The feasibility and effectiveness of the proposed model and solution method are verified through the numerical example and simulations. The results indicate that the trade-offs between travel time and charging cost significantly influence the proposed solution, and the residual energy at the destination affects the availability of routes. The policy implications for the BEV-based intercity travels are discussed.

Understanding the urbanization impacts of high-speed rail in China

Advances in transport technology have been shown to play a vital role in urban development over millennia. From the engineering and pavement innovations of the Roman road network to the aerospace breakthroughs that enabled jet aircraft, cities have been reshaped by the mobility changes resulting from new designs for moving people and goods. This article explores the urbanization impacts of High-Speed Rail’s introduction in China, which has built the world’s largest High-Speed Rail network in record time. Since High-Speed Rail was launched in Japan in 1964, this technology has worked to reshape intercity travel as a revolutionary transportation alternative. High-Speed Rail has developed steadily across Japan, France, Germany, Italy, Switzerland during the 1970s and 1980s. It expanded to Russia, Spain, the United Kingdom, and Sweden in the 1990s. In the 21st century, China began developing High-Speed Rail on an unprecedented scale, and now has a national network that is longer than the totality of the rest of the world’s High-Speed Rail operations combined. China’s High-Speed Rail operation is exerting a transformative influence on urban form and function. This article synthesizes secondary research results to analyse the impacts of HSR on urbanization. These effects include population redistribution, urban spatial expansion and industrial development. We offer a typol-ogy that considers the urban effects of High-Speed Rail at three spatial levels: the station area, the urban jurisdiction, and the regional agglomeration. When organized through our typology, research findings demonstrate that High-Speed Rail influences urban population size, urban spatial layout and industrial development by changing the acces-sibility of cities. We highlight the processes by which High-Speed Rail ultimately affects the urbanization process for people, land use, and industrial development. However, High-Speed Rail’s impacts on urbanization are not always positive. While leveraging the development opportunity enabled by High-Speed Rail, governments around the world should also avoid potential negative impacts by drawing lessons from the experience of High-Speed Rail’s rapid de-ployment in China.

Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries.

ObjectiveCountries have adopted different approaches, at different times, to reduce the transmission of coronavirus disease 2019 (COVID‐19). Cross‐country comparison could indicate the relative efficacy of these approaches. We assess various nonpharmaceutical interventions (NPIs), comparing the effects of voluntary behavior change and of changes enforced via official regulations, by examining their impacts on subsequent death rates.Data SourcesSecondary data on COVID‐19 deaths from 13 European countries, over March–May 2020.Study DesignWe examine two types of NPI: the introduction of government‐enforced closure policies and self‐imposed alteration of individual behaviors in the period prior to regulations. Our proxy for the latter is Google mobility data, which captures voluntary behavior change when disease salience is sufficiently high. The primary outcome variable is the rate of change in COVID‐19 fatalities per day, 16–20 days after interventions take place. Linear multivariate regression analysis is used to evaluate impacts.Data collection/extraction methods: publicly available.Principal FindingsVoluntarily reduced mobility, occurring prior to government policies, decreases the percent change in deaths per day by 9.2 percentage points (pp) (95% confidence interval [CI] 4.5–14.0 pp). Government closure policies decrease the percent change in deaths per day by 14.0 pp (95% CI 10.8–17.2 pp). Disaggregating government policies, the most beneficial for reducing fatality, are intercity travel restrictions, canceling public events, requiring face masks in some situations, and closing nonessential workplaces. Other sub‐components, such as closing schools and imposing stay‐at‐home rules, show smaller and statistically insignificant impacts.ConclusionsNPIs have substantially reduced fatalities arising from COVID‐19. Importantly, the effect of voluntary behavior change is of the same order of magnitude as government‐mandated regulations. These findings, including the substantial variation across dimensions of closure, have implications for the optimal targeted mix of government policies as the pandemic waxes and wanes, especially given the economic and human welfare consequences of strict regulations.

Prinsip Perencanaan dan Kriteria Pengendalian Pengembangan Fasilitas Rest-Area pada Jalan Tol di Indonesia

Stopovers and relaxing facilities are important in intercity travel on toll roads. Long distances travel times in toll roads increase the supply and demand of these facilities in Indonesia. Not only the needs for refueling and resting, but also new support facilities demanded in rest area are often found, such as shopping, restaurant, worship place, and others. This phenomenon has attracted many investors to establish rest area with those complex facilities in term of business profit. To prevent negative impact of development and to protect main function of toll roads, this paper will discuss the planning and development control principles of rest areas located on toll roads in Indonesia. Analysis of components and criteria for rest areas on toll roads are generated by analyzing problems occurred. Site location, site area, accessibility, and main facilities specification are absolute factors, while zoning, circulation, road signage, and site utilities are conditional factors. The planning principles is to guarantee safety, security, and convenience of all activities in and around rest area and toll road. This research describes several criteria and components that should be controlled in order to achieve rest area planning principles that determined by qualitative methods.

Personalized Multimodal Travel Service Design for sustainable intercity transport

Efficient multimodal public transport has been recognized as an effective solution for realizing sustainable intercity transport. However, it has not been well accepted by the general public due to the poor coordination among transport modes, inconvenience caused by transfers, difficulties on route plannings, etc. To address the problem and facilitate the popularity of multimodal intercity travel, this research proposed the design of personalized multimodal travel service based on SPSS (Smart Product Service System). It not only could provide multimodal route recommendations based on individual preferences, but also could improve its performance along usage. Meanwhile, the influencing factors of multimodal intercity travel is investigated, based on which the travel choice model is developed. Besides, a few-short learning method was proposed, which could mine the individual preference even from limited historical travel data. Finally, an experimental case study was conducted that demonstrates the effectiveness of the proposed solution.

The greening of intercity travel: Environmental protection subsidy and HSR operation planning

The transportation sector is one of the highest sectors responsible for negative environmental impacts in the cities. It is necessary for the government to inspire the local transportation system to reduce its pollution emissions. Therefore, this article uses the environmental protection subsidy (EPS) to attract passengers to choose green travel. In order to obtain the optimal solution for green transportation, we propose a bi-level multi-objective optimization model based on the collaborative optimization between EPS coefficient and high-speed rail (HSR) operation planning. There are three main game players, namely the government, HSR enterprise, and passengers. We develop a hybrid optimization algorithm to solve the model and verify this proposed methodology by using the Beijing-Tianjin-Hebei intercity transportation network. Our results show that the EPS has a significant impact on intercity travel. As demand increases, the EPS coefficient gradually decreases from 0.85 to 0. HSR's profit growth is higher than the government subsidies.

The Impacts of High-Speed Rail on Regional Accessibility and Spatial Development—Updated Evidence from China’s Mid-Yangtze River City-Cluster Region

There has been long and ongoing interest in the impacts of high-speed rail (HSR) on regional spatial development. Most existing studies, however, reported findings at relatively coarse geographic scales, i.e., at the prefecture-city or above level in the Chinese context. This paper presents the empirical evidence of HSR impacts from the county-level cities in China’s Mid-Yangtze River City-Cluster Region (MYRCCR). The study utilized rail time data and the socio-economic data for MYRCCR’s 185 county-level cities in the years of 2006 (without HSR) and 2014 (with HSR) and analyzed the impacts of HSR on inter-city travel times, accessibility, spatial inequality, and regional economic linkages among the MYRCCR cities. The results show that, from 2006 to 2014, HSR reduced city-to-city average travel time by 34.5% or 124 min and improved accessibility to all cities in the MYRCCR. HSR’s impacts on accessibility and spatial equality exhibited a scale-differentiated pattern. MYRCCR-wide, HSR transformed a pattern of spatial polarization towards the one of corridorization. Cities located on major HSR corridors became more balanced in 2014 than in 2006. Nevertheless, at the county-city level, the gap between cities with the most and the least accessibility gains was much greater than the gap between those with the largest and the smallest travel time savings. Attributable to HSR services, the intensity of economic linkage increased between MYRCCR cities, especially between the provincial capital cities and those on the major lines of the national HSR grid, which implies an emerging process towards territorial cohesion in MYRCCR. National, provincial, and local governments should consider transportation as well as non-transportation policies and measures to direct HSR impacts towards further enhanced spatial development and regional equality.

Hierarchical Passenger Hub Location Problem in a Megaregion Area Considering Service Availability

The rapid growth of the intercity travel demand has resulted in enormous pressure on the passenger transportation network in a megaregion area. Optimally locating hubs and allocating demands to hubs influence the effectiveness of a passenger transportation network. This study develops a hierarchical passenger hub location model considering the service availability of hierarchical hubs. A mixed integer linear programming formulation was developed to minimize the total cost of hub operation and transportation for multiple travel demands and determine the proportion of passengers that access hubs at each level. This model was implemented for the Wuhan metropolitan area in four different scenarios to illustrate the applicability of the model. Then, a sensitivity analysis was performed to assess the impact of changing key parameters on the model results. The results are compared to those of traditional models, and the findings demonstrate the importance of considering hub choice behavior in demand allocation.

Evolution of the Chinese spring festival travel network during the COVID-19 early outbreak

ABSTRACT We investigate the evolution of the Chinese Spring Festival travel network during COVID-19 early outbreak (Jan 17 to Feb 23, 2020) in China and explore the impacts of COVID-19 on the evolution of the intercity travel network. We find that the connections of a China city with other 27.9 cities were cut off after the Wuhan lockdown (Jan 23). Correlating the degree centrality of each city and the cumulative numbers of COVID-19 confirmed cases, we find that cities with confirmed cases have 87.63 more indegrees and 97.13 more outdegrees than cities without confirmed cases. We also find that Jan 24 (one day after the Wuhan lockdown) has the highest effect of cumulative cases on indegree that is one additional confirmed case corresponds to 0.159 decreases in indegree. In terms of outdegree, Feb 2 has the highest effect that is one additional confirmed case corresponds to 0.022 decreases in outdegree.

Dynamic governance decisions on multi-modal inter-city travel during a large-scale epidemic spreading

Within half a year, COVID-19 spreads to most countries in the world, as well as posed a great threat to the public health of human beings. The implementation of non-pharmaceutical intervention (NPI), including travel ban, proved to be an effective way for controlling the epidemic spreading, e.g., the ban of inter-city transportation stops transporting virus through passengers between cities. However, travel ban could significantly impact many industries, e.g. tourism and logistics, thus jeopardizing the regional economy. This paper focus on assisting the national or regional government to make dynamic decisions on restricting and recovering intercity multi-modal travel services. Our model can characterize impacts of inter-city traffic on the spread of the COVID-19, as well as on the regional economy. By applying a reinforcement learning approach, we develop an online optimization model to identify the modal-specific travel banning strategy that can balance the epidemic control as well as the negative impacts on regional economy. The numerical study based on a network of multiple cities in China shows that the proposed approach can generate better strategies compared with some existing methods.

On COVID-19-safety ranking of seats in intercontinental commercial aircrafts: A preliminary multiphysics computational perspective.

The evolution of coronavirus disease (COVID-19) into a pandemic has severely hampered the usage of public transit systems. In a post-COVID-19 world, we may see an increased reliance on autonomous cars and personal rapid transit (PRT) systems, with inherent physical distancing, over buses, trains and aircraft for intracity, intercity, and interstate travel. However, air travel would continue to be the dominant mode of intercontinental transportation for humans. In this study, we perform a comprehensive computational analysis, using ANSYS Fluent, of typical intercontinental aircraft ventilation systems to determine the seat where environmental factors are most conducive to human comfort with regards to air quality, protection from orally or nasally released pollutants such as CO2 and coronavirus, and thermal comfort levels. Air velocity, temperature, and air pollutant concentration emitted from the nose/mouth of fellow travelers are considered for both Boeing and Airbus planes. In each plane, first class, business class, and economy class sections were analyzed. We present conclusions as to which is the optimum seat in each section of each plane and provide the data of the environmental conditions to support our inferences. The findings may be used by the general public to decide which seat to occupy for their next intercontinental flight. Alternatively, the commercial airliners can use such a model to plan the occupancy of the aircraft on long-duration intercontinental flights (viz., Airbus A380 and Boeing B747).Electronic Supplementary MaterialSupplementary material is available for this article at 10.1007/s12273-021-0774-y and is accessible for authorized users.

Investigation of factors and their dynamic effects on intercity travel modes competition

The objective of this paper is to explore the effects of the influencing factors on the intercity travel mode competition and evaluate their dynamic effects across the travel distance. Passengers’ activity data over the whole process of intercity travel were collected for analysis. A Bayesian binary logistic model was developed to identify the significant factors associated with the intercity travel mode competition. An elasticity analysis was applied to quantitatively analyze the dynamic effects of the significant factors on the intercity travel mode competition. The Bayesian binary logistic models results showed that factors such as the travel distance, intercity travel cost, intercity travel time, safety, comfort, punctuality, access time, and departure time have significant impacts on the intercity travel mode competition. Moreover, the elasticity analysis results showed that the elasticity of the factors varies across the travel distance, and each factor has a heterogeneous effect on the travel mode market share across the travel distance. These findings can help in the implementation of more effective management strategies for intercity travel mode planning.

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

BackgroundAs 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.MethodsIn 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.ResultsWe 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.ConclusionsTo 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.