Intercity Travel Demand Research Articles

Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand

Electric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.

Deploying Public Charging Stations for Battery Electric Vehicles on the Expressway Network Based on Dynamic Charging Demand

There is an obvious gap between the rapid growth of battery electric vehicle (BEV) intercity travel demand and the worse deployment of charging facilities on the expressway network. With the consideration of dynamic charging demand, a bilevel model is proposed to deploy charging stations for the expressway network. The upper model aims at determining the location of charging stations and the number of chargers in each station to minimize the construction cost and total BEV travel cost. The dynamic charging demand is obtained by the lower model, which is constructed as a multiclass dynamic traffic assignment model, including charging, queuing, and flow transmission processes. A genetic algorithm incorporating the method of successive averages is adopted to solve the bilevel model. A real case in the Shandong province of China is employed to evaluate the effectiveness of the proposed model and algorithm. The sensitivity analyses show that a high level of charging service can encourage the usage of BEVs. In addition, when the BEV percentage is at a low level, planners should give priority to the quantity and location to expand charging service coverage and BEV’s travel range; then, with the increasing of BEV percentage, the construction emphasis should change to charging station’s capacity.

The interaction effect of severe weather and non-weather factors on freeway travel volume

The existing literature has confirmed that extreme weather such as wind, rain, and snow have a negative impact on cross-sectional traffic flow. However, travel activities with different destination regions, travel distances and vehicle types may have different responses to severe weather. We employ the multilevel mixed-effects negative binomial (MENB) model to explore the interaction effect of severe weather and non-weather factors on intercity origin-destination (OD) demand, based on the data from freeway toll stations in Shandong Province from March 2011 to February 2012. The MENB model is superior to the SNB model in that the former has both smaller AIC (456,645.4 < 4,586,877.2) and BIC (456,963.4 < 458,975.8) values and log-likelihood ratio tests. The results indicate that weather impact on freeway travel to the same metropolitan area is homogeneous, and the impact in different metropolitan areas is heterogeneous. Besides, there is an interaction effect between severe weather (strong wind, fog, heavy rain, snow) and travel distance on freeway OD volume. With increasing travel distance, the impact of both strong wind and heavy rain decreases gradually, while the impact of both fog and snow increases. In addition, heat (0.0351 > 0.0201), strong wind (0.0930 > 0.0454), and heavy rain (0.1245 > 0.1044) have a greater impact on passenger car volume than on truck volume, while fog (0.4340 < 0.4802) and snow (0.4383 < 0.4884) have a less significant impact on passenger car volume. The findings of this study provide some deep insights into the relationship between severe weather factors and intercity travel demand, which suggests that different strategies of travel demand management should be adopted for different travel distances and different vehicle types under various weather conditions.

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.

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.

Impact of weather on freeway origin-destination volume in China

Existing literature has confirmed that severe weather events have a negative impact on travel behavior. Considering the features of long distances and long durations, intercity travel may be affected by the weather at the origin (O) point and at the destination (D) point. This paper aims to reveal the relationship between weather and OD volume of intercity travel by additional considerations of the weather difference between OD points and the lagging effect of various severe weather conditions. Travel data from toll collection system of freeway network in Shandong Province of China is extracted to investigate the weather effect on intercity travel demand. Three types of real-time weather variables (continuous variables of basic weather, dummy variables of severe weather, and weather difference variables between OD points) and two types of non-real-time weather variables (lagging variables and advance variables of severe weather) are combined to systematically analyze the weather effect. The results show that, temperature value and wind speed have no significant impact on intercity travel. Hot day, cold day, foggy day, strong breeze, heavy rain, rainfall, snowfall and visibility have significant impacts on freeway OD volume. Among these weather events, fog, heavy rain and snow usually have the most severe impact. Except for the weather at the O point, the differences in rainfall and snowfall between OD points have significant impacts on intercity travel demand, while the differences in temperature, wind speed, visibility between OD points do not. In addition, snow and heavy rain have the lagging and advance effects on freeway OD volume. The advance effect of snow and heavy rain lasts for one day and the lagging effect lasts for two days. Intercity travel demand is very sensitive to adverse weather on weekend days and during the afternoon. Comparative analysis of multiple OD pairs indicates that intercity travelers with less relevant experience are more sensitive to adverse weather. Findings from the paper will provide transport institutions with a practical guide for systematically investigating the weather effect on intercity travel demand. It can be applied in the intelligent transportation system to provide long-distance travelers with more accurate weather information and traffic guidance services.

Success factors in the introduction of an intermodal passenger transportation system connecting high-speed rail with intercity bus services

This study analyzes the success factors of the introduction of an intermodal intercity passenger transportation system in the Kyusyu region, Japan, in March 2011. This system, called “B&S Miyazaki,” integrates an intercity bus service with a high-speed rail service connecting the cities of Miyazaki and Fukuoka. B&S Miyazaki has successfully captured induced intercity travel demand without causing the existing travel modes to lose their ridership. Starting with interviews with major stakeholders, the introduction process for the new system was analyzed in three stages. The results of the analysis unveiled five success factors: (1) the acceptance of a new intermodal system in the intercity transportation market from both users and suppliers; (2) clear incentives for cooperation among stakeholders with expected benefits from asset-sharing and joint marketing activities; (3) an excellent intermodal service package including the short transfer distance, a unified ticketing system, an integrated timetable and schedule adjustment, special discounting, and the coordinated design of train/bus bodies; (4) a win–win solution for the shortcomings of the existing travel modes; and (5) a learning process about the effects for existing operators with flexible operator strategy adjustments.

Impact of high-speed rail on intercity travel behavior change: The evidence from the Chengdu-Chongqing Passenger Dedicated Line

This paper investigates the impact of high-speed rail (HSR) on intercity travel behavior changes using the Chengdu-Chongqing (Chengyu) Passenger Dedicated Line (PDL) as an example. Based on the statistical analysis of survey data that consists of 1384 samples, the result shows that HSR has become the primary mode of intercity travel between Chengdu and Chongqing. Specifically, travel demand has increased by 60% after the operation of the HSR system and the demand change is affected by several factors, such as trip purpose, gender, and travel time savings. The result also shows that the variation of travel demand in both directions declined after the opening of HSR, which suggests that HSR tends to promote a convergent growth of intercity travel demand. Although most passengers ride the Chengyu HSR for a personal reason, the number of riders using it for commuting and business purposes has also increased substantially. Overall, the study confirms that the economic link between Chengdu and Chongqing has been enhanced by the operation of HSR.

Estimation of inter-city travel demand for public road transport in Nigeria

This paper models an intercity travel demand which uses the principal structural variables that have been identified in the literature. This research develops an urban public travel demand model for 19 directional O-D city-pair network originating from Owerri Urban. Using revealed preference data from the period of 2014 to 2016 which was filtered into 207 observations operated by the 16 transport companies in Owerri, Imo State. The empirical analysis explicitly modelled the pattern of correlations among service variables by a log-linear model using OLS. The estimates yield better demand elasticities than those of direct linear models. Other empirical findings include that; overall fares elasticities are low, so that increases in fare levels will almost always lead to increases in revenue. However, it is possible for any would-be operator determine the expected patronage on any route of operation using the derived models from this paper. Thus this paper has come up with empirical model to assess the viability of intercity passenger transport operation in Nigeria. However, it will also help the operators in business to do a sensitivity analysis based of changes in the intercity passenger travel markets in Nigeria.

An assessment of the CO2 emissions reduction in high speed rail lines: Two case studies from Turkey

Transportation sector is one of the major emitters of greenhouse gases due to high consumption of fossil fuels in passenger and freight transportation. In recent years, high speed rail systems (HSR) have become an important policy option for intercity transportation in many countries, including Turkey, as they have relatively low emissions impact. Currently, the total HSR network in Turkey extends to 1213 km and connects 7 cities mostly located along a main HSR corridor. Annual ridership of the first two lines, Ankara-Eskişehir (ANK-ESK) and Ankara-Konya (ANK-KON), have reached almost 2 million passengers each, while total ridership in all four lines was about 3.5 million passengers in 2014. With the upcoming two new HSR lines, total length of HSR network will exceed 3000 km by 2020.Despite the increase in ridership, no effort has been conducted to evaluate HSR from environmental perspective, which was the main motivation, and has been chosen as the scope of this study. As a first step, the CO2 emissions reduction performance of the HSR lines in Turkey was determined based on a proposed line-based methodology. The method included the determination of i) current emissions of HSR lines, ii) estimated emissions from the alternative modes expected to be used in No-HSR situation, as stated in a HSR user survey, and iii) the difference in emissions as the HSR reduction performance. Calculation of an average CO2 emissions reduction value per passenger-km travelled (PKT) enabled determination of emissions reduction potential of the existing and upcoming HSR lines under estimated future demands in the last step. The proposed methodology not only considered CO2 emissions from alternative modes, but also factored in the effect of intercity travel demand induced by HSR observed at this early development stage in Turkey.The results suggested that HSR caused a total reduction of 24.3 ktCO2 currently on two study corridors, and may even result in a reduction of 452.7 ktCO2 in 2023, if estimated ridership is realized in all lines. Line based analyses showed that HSR performance in reducing CO2 emissions was limited as highly demanded HSR lines currently served short routes and mostly caused modal shift from bus services, which were also efficient compared to car. The CO2 emissions reduction potential of future HSR services can be higher if a) new HSR lines can create a network effect along the main corridor and b) supplementary policies can be developed to generate high HSR demand that would be shifted from car, and even air, on the longer routes.

Analysis of intercity travel demand by combining sources of preference data

Road transport is the only available alternative for intercity trips to or from the city of Ocana (Norte de Santander - Colombia). Undesirable conditions, such as landslides and road lane losses, illegal blockades due to a lack of public order and accidents, result in temporary or total isolation of access roads to the city causing traffic jams, isolation and economic losses to travelers who want to leave or enter the municipality. Given these limited transport conditions, the research aims to model the behavior of the modal choice of intercity transport in Ocana City, through: calibrating logit models, and using revealed preference (RP) and stated preference (SP) data. The research seeks to examine the sensitivity of travellers’ behavior when faced with hypothetical choice scenarios, given the implementation of air transport routes that lead to the five most frequent destinations: Bogota, Medellin, Barranquilla, Bucaramanga and Cucuta. The investigation revealed the significance of interactions among variables associated with the level of service, socio-economic variables and travel characteristics, which were found to be relevant at the time of making a modal choice in an inter-city context, in addition to the acceptance of air transportation.

Typology of Travelers Based on Their Annual Intercity Travel Patterns Developed from 2013 Longitudinal Survey of Overnight Travel

The ability to forecast and to plan proactively for intercity travel patterns is a critical topic for many metropolitan areas. This paper sets forth, for use in travel demand modeling, a new typology of travelers on the basis of their annual intercity travel patterns. The typology was developed by using the 2013 Longitudinal Survey of Overnight Travel, which provided a year’s data and addressed the nonroutine nature of these long-distance trips. Annual summaries of intercity trips were compared, and travelers were categorized into six distinct segments by using the K-means cluster analysis. Each group clearly demonstrated a unique relationship with intercity travel and mode choices. A comparison of the typology with commonly used demographic variables highlighted the fact that such a characterization provided a deeper understanding of traveler preferences and biases than simply using demographics. The distribution of respondent demographics was relatively similar across each cluster and highlighted the need to consider factors beyond basic demographics when factors influencing long-distance travel decisions are being identified. This typology can significantly improve intercity demand models, either as an independent variable in disaggregate intercity travel demand models or as a region-level behavior-based characterization that provides meaningful contributions to the planning process. Specifically, the annual overnight intercity travel typology provides more information on travelers’ motivations and biases than other sociodemographic variables and thereby allows the model to be more responsive to policy and planning analyses and to provide more accurate forecasts.

Estimation of mode choice models in the region of Rodopi, North-Eastern Greece

Disaggregate mode choice models estimation, which are used for evaluating the ridership share on intercity travel service and for identifying modes which will lead to an upgraded traveler's service, constitutes a critical part of evaluating alternative travel service proposals to intercity travel. The aim of this paper is to estimate the choice of mode in intercity travel demand. Disaggregate logit models are estimated by using data collected in order to investigate the passengers' behavior of the broader region under study. The final models are applicable on regional level and have statistics within acceptable region.

Intercity Travel Demand Analysis Model

It is well known that intercity travel is an important component of travel demand which belongs to short distance corridor travel. The conventional four-step method is no longer suitable for short distance corridor travel demand analysis for the time spent on urban traffic has a great impact on traveler's main mode choice. To solve this problem, the author studied the existing intercity travel demand analysis model, then improved it based on the study, and finally established a combined model of main mode choice and access mode choice. At last, an integrated multilevel nested logit model structure system was built. The model system includes trip generation, destination choice, and mode-route choice based on multinomial logit model, and it achieved linkage and feedback of each part through logsum variable. This model was applied in Shenzhen intercity railway passenger demand forecast in 2010 as a case study. As a result, the forecast results were consistent with the actuality. The model's correctness and feasibility were verified.

Logit Models for Forecasting Nationwide Intercity Travel Demand in the United States

Nested and mixed logit models were developed to study national-level intercity transportation in the United States. The models were used to estimate the market share of automobile and commercial air transportation of 3,091 counties and 443 commercial service airports in the United States. Models were calibrated with the use of the 1995 American Travel Survey. Separate models were developed for business and nonbusiness trip purposes. The explanatory variables used in the utility functions of the models were travel time, travel cost, and traveler's household income. Given an input county-to-county trip demand table, the models were used to estimate county-to-county travel demand by automobile and commercial airline between all counties and commercial-service airports in the United States. The model has been integrated into a computer software framework called the transportation systems analysis model that estimates nationwide intercity travel demand in the United States.

Fitting a multiple regression line to travel demand forecasting: The case of the prefecture of Xanthi, Northern Greece

In the last decades, there has been substantial development in modeling techniques of travel demand estimation. Most of the techniques concerning the construction and the calibration of these models, where developed in industrialized countries of the world. In Greece, the construction and calibration of intercity travel demand follow mostly either the extrapolation of historical data, such as growth rates, or the recalibration of models initially formulated and estimated for scenarios in industrialized countries. The aim of this paper is to identify and estimate the main variables, which affect the travel demand, and to develop models to predict it. The study is focused in the intercity passenger transportation concerning the prefecture of Xanthi in Northern Greece. The models that have been developed and calibrated are aggregate and they are based on multiple linear regression analysis. The final demand models have statistics within the acceptable regions and, also, they are conceptually reasonable.

A study of an integrated intercity travel demand model

It is well reported that induced travel is an important component of travel demand. With improved transportation conditions, short run effects (e.g., route switches, mode switches, changes of destination, and new trip generation) and long term effects (e.g., change in household auto ownership, and spatial reallocation of activities) will be observed. This paper aims to develop an integrated intercity travel demand modelling system suitable for substantial changes in service level. The model utilizes combined estimation across multiple data sources such as SP, RP and aggregate data. This integrated intercity travel demand modelling system is characterized by an explicit intercity travel behavioural framework and its ability to capture induced travel. Intercity travel decisions are represented by a nested model structure, and an accessibility measure is introduced to capture short term induced travel. The paper also sketches a way to estimate induced travel resulting from long term changes (spatial reallocation of activities). As a case study, an integrated model including trip generation, destination choice, mode choice, and route choice is presented for an intercity high speed rail project planned in Japan, and short term induced travel elasticities with respect to travel cost, travel time, and etc. are also presented.

The Trouble with Intercity Travel Demand Models

The current state of operational practice in intercity travel demand modeling was critically reviewed. Conventional model structures and specifications as well as a range of theoretical and practical problems with current models were studied. These included problems with the total demand model specification, modeling intercity mode choice with nested logit models, modeling access and egress trips to and from common carrier line-haul modes, forecasting the demand for new modes of travel, dealing with highway congestion, aggregation issues in intercity models, and data collection issues. The study concluded with some suggestions about promising research and development directions for model improvement.

An aggregate demand model for intercity passenger travel in Sri Lanka

Inspite of the inherent weaknesses in aggregate demand models, they continue to be used in everyday applications, especially in developing countries. The largely data intensive disaggregate model preclude its application in many cases. This paper attempts the formulation and calibration of an aggregate total demand model for estimating inter-district passenger travel by public transport in Sri Lanka. In its process, an investigation is made of the common problems in the aggregate approach while examining possible remedial measures to improve the accuracy and (hence) the usability of the aggregate model. It is argued that commonly used variables and functional forms are inappropriate for making accurate estimates in developing countries. Consequently, the model calibration is shown to incorporate variables representing urbanisation, under-development, transfers, a mode-abstract cost function and intrinsic features. The necessity for functional form for each variable to be based on behavioral assumptions that are tested using the Box-Cox transformation for ensuring the best fit of the data is also observed. Although, the model form was calibrated for Sri Lanka, the model is generalised in order for its applications to other countries as well as, both, inter-district and intercity travel demand estimation.

Transferability of intercity disaggregate mode choice models in Canada

In many transportation studies, the time span of data collection, model development, and analysis is often too long to be responsive to the needs of policy analysts and decision makers. This problem is often exacerbated in situations with severely constrained analysis resources. Therefore, it is often useful to transfer a model from one area to another. Model transfer is defined as the application of a model developed in one area to describe the corresponding behavior in another area. This paper examines the transferability of a class of models used in intercity travel demand analysis. Specifically, disaggregate mode choice models of the multinomial logit type are developed for two regions in Canada, and some established measures of transferability are applied to assess the potential of calibrating these models in one region and applying them in the other. Comparison of mode choice models estimated on data sets from the two regions yielded inconclusive results regarding model transferability. In general, transferred models were found to be 18–23% less accurate than local models in predicting modal shares. Adjusting models' parameters to reflect observed modal shares in the application context improved the predictive ability of the models by about 10%. Key words: transferability, mode choice, disaggregate, travel behavior, multinomial logit, intercity.