Long-distance Travel Demand Research Articles

Tradable mobility credits for long-distance travel in Europe

There is an urgent need to devise policy instruments that will reduce GHG emissions to a sufficient extent in order to achieve the climate targets. Approximately 16% of all greenhouse gas emissions are generated by long-distance travel. Our objective is to estimate the impact of a Tradeable Mobility Credit (TMC) scheme − where the total emissions are fixed by design whereas the price is the outcome of travelers’ choices − will have on modal split and trip cancellation for long-distance leisure travel in Europe. To this end, we develop a market equilibrium model which accounts for travel and trading decisions. For our case study consisting of a travel demand of more than 300 million trips performed annually between 3,000 city-pair connections, the credit price that emerges in the market equilibrium state is 272€ per ton of CO2 under an emission reduction target of 30%. Due to the TMC, overall long-distance leisure travel demand is reduced by 20% and the share of air among the remaining trips decreases from 50 to 42% whereas the market share of rail increases from 23 to 26%. Modal shifts and trip cancellation rates vary greatly amongst OD-pair connections, depending on the local value-of-time and the extent of modal competition for the respective connection. Our findings contribute to the on-going debate surrounding instruments for stimulating sustainable (im)mobility, in particular in the context of the long-distance travel market.

An analysis of EV charging and route choice behavior considering the effects of planning ability, risk aversion and confidence in battery in long-distance travel

The increasing demand for long-distance and intercity travel is driven by the progress in regional transportation integration and the establishment of charging infrastructure. This increase suggests that the use of electric vehicles (EVs) for long-distance trips is likely to become more prevalent in the future. Therefore, there is an urgent need for more studies on the travel behavior of EV drivers for long-distance trips. However, there are few studies looking at the travel behavior of EV drivers on long-distance trip and multiple charging during trips. To fill this gap, this study identified EV drivers’ preferences for charging and route choice in long-distance or intercity travel on the basis of a stated preference survey, with the Shandong highway network as the travel scenario. A nested logit (NL)-based hybrid choice model was used to investigate the impacts of personal attitudes, namely planning ability, risk aversion and confidence in battery, on the drivers’ charging times choice behavior. The NL structure consists of two levels, with the upper level focusing on the charging times decision process, and the lower level analyzing the choice behavior of routes with no charging, routes with charging once, and routes with charging twice. The results show that the initial state of charge, and attitudes significantly influence the decision on charging times. In addition, EV drivers who are risk-averse and lack confidence in the battery tend to charge, whereas drivers with strong planning ability prefer to charge once in long-distance travel. Charging and resting simultaneously is the best way to meet the needs of charging and resting, and reduce the time lost while driving. These results provide insights on drivers’ travel planning and charging infrastructures planning for long-distance trips.

Workshop Synthesis: Innovations in travel survey collection to support long distance and tourism travel analyses

As researchers and practitioners seek to better understand and proactively plan for long-distance travel demand, they need data that characterizes the breadth and depth of long-distance travel decisions. In this workshop, we reviewed work from five presentations to draft guidance for integrating these sources to support travel analyses. Our workshop specifically considered (a) what data is needed for long distance and tourism travel analyses (including traveler profiles, trip characteristics, external travel, visitor travel, destination choices, mode choices, environmental impacts, economics, etc.), (b) the most appropriate methods for capturing this data, and (c) how to best integrate these different methods.

Identifying and understanding long-distance travel demand by combining official transport statistics and survey data

While much is known about everyday travel of the German population, long-distance travel is still underreported. The main data source, the national travel survey “Mobility in Germany (MiD)”, cannot simply be used to describe the demand: complex extrapolations and complementary data are necessary to obtain a consistent picture. The presented approach of ‘data fusion’ integrates different data sources to provide the overall long-distance travel demand.The result reveals that almost half of the total transport performance of the residential population in Germany (46 % of passenger kilometers) is accounted for by trips of at least 100 km (one-way distance).

Mind the gender gap in ride-hailing from the demand side

Improving the transport system to enhance women's access to social opportunities and services has been a key initiative in mitigating gender inequality. Studies have examined women's different travel demands and experiences from men, and the mismatch between women's demands and transport services. However, little attention has been given to the gender gap in ride-hailing usage in the context of the fast development of platform economy-based new transportation services. Thus, this paper examines the nexus between gender and inequalities in ride-hailing from the demand side. Two key questions are explored: if ride-hailing serves women more/less, how does this gender difference in using ride-hailing occur? Does the emergence of ride-hailing mitigate or deteriorate the existing gender gap in capability to move? An innovative integration of big data and time-space geography approaches is developed to examine the multi-source data collected from Chengdu, China. The modelling results indicate that ride-hailing emerges as an affordable travel mode addressing women's demand for long-distance travel in the Chinese context of women's high labour participation and thus the existence of a considerable number of employed women with a sizeable daily activity space and a considerable income. The gender gap in capability to move is thus mitigated by ride-hailing at an aggregate level. This paper calls for a more nuanced and context-specific understanding of how ride-hailing may provide challenges and opportunities to gender equity in daily travel.

The environmental challenges of long-distance transport in Europe: prospects for the 2020s

This paper reviews recent trends in long-distance passenger travel demand in the European, and prospects to reduce its associated greenhouse gas emissions. Key changes in the last years include the expansion of long-distance bus services in several countries, the plateauing of per capita car travel, so that interurban car mobility has stabilized in many countries, the expansion of high-speed rail, although with modest results in terms of modal change, and the pervasive growth of low-cost air travel, including the intercontinental segment. Supplier-induce demand is becoming a key trait in long-distance travel, as a growing number of commercial airlines and high-speed train operators replicate the successful strategies of low-cost airlines. These trends compromise the European Union's objectives in climate change mitigation.

Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation

Every sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO<sub>2</sub> emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO<sub>2</sub> emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO<sub>2</sub> emissions. Potential strategies and policies to reduce air travel demand and its CO<sub>2</sub> emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO<sub>2</sub> emissions from transport by 7.5%.

Responding to the climate emergency: how are UK universities establishing sustainable workplace routines for flying and food?

ABSTRACT Scope 3 emissions from the UK higher education sector are globally significant, and long-distance air travel and catering are particularly emissions-intensive aspects of workplace routine. They each present complex problems, as transition to low-carbon alternatives requires the reconfiguration of professional practices. This paper examines the sustainability policies of 66 UK universities to establish the extent to which planning and action in these areas are commensurate with climate emergency declarations. The findings indicate that universities recognize their role in creating demand for long-distance travel and sustaining high-carbon diets. However, few have specific emissions reduction targets or action plans that would rapidly and substantially reduce emissions in these areas. Discussion focuses on two core points; first, how greater cohesion in reporting and target-setting can be achieved across the sector to raise the ambition of targets and intervention; and second to identify opportunities for institutions to disrupt and reshape professional practices to reduce emissions in these areas. Key Policy Insights Reducing emissions in the higher education sector requires organizations to foster low-carbon academic practices by engaging with the systemic cultural and material conditions that support high-carbon academic practices. The establishment of robust targets, action plans and monitoring processes would further support sector-wide decarbonization, and require consensus across HE institutions and governing bodies. Sector-wide agreement on the level and pace of emissions reduction will help to accelerate ambition regarding Scope 3 emissions reduction and determining the appropriate contribution of different institutions will help identify where action is most urgently required. Findings suggest a need for absolute targets for emissions reduction associated with long-distance travel, and that food policies focus on achieving a volumetric reduction in the weight of meat served so that absolute levels of greenhouse gas emissions are reduced. Travel and food provision are complex aspects of university emissions, but a climate emergency framing requires all organizations to use their full range of influence to rapidly and substantially reduce emissions.

Hyperloop transport technology assessment and system analysis

ABSTRACT The Hyperloop concept, pod speed competitions and current project developments have recently attracted much publicity. In this paper the transport technology of the vacuumed tube transport project Hyperloop is assessed through a system analysis of its principal aims, functional design, transport capacity and demand in comparison with existing commercial airlines, high-speed rail, and Maglev lines. First, the potential for high-speed long-distance travel demand for Hyperloop based on existing airline transport volumes between major airports in Germany on the one hand, and the proposed Hyperloop link from Los Angeles to San Francisco in California on the other, is assessed in general terms. Second, the technical feasibility of the proposed Hyperloop concept for vehicle design, capacity, operations, propulsion, guidance, energy supply, traffic control, safety, alignment, and construction is discussed in more detail. Third, possible environmental impacts and uncertain investment, operating and maintenance costs for implementation of a Hyperloop line are described. Finally, the risks for further Hyperloop project development and the need for more transparent research are emphasized.

Scenarios for transitioning cars from ICEV to BEVs and PHEVs using household level GPS travel data

There has been a continuous effort to quantify the expected greenhouse gas reductions from plug- in electric vehicles (PEVs). Most existing studies are based on standardized driving cycles to compare the performance of PEVs and conventional internal combustion engine (ICE) vehicles. This study is based on assigning the most efficient plug-in hybrid or full electric vehicle to a household based on the household travel behavior as collected by GPS tracking data of thousands of vehicles’ real trips in California, and it found that PEV adoption can reduce greenhouse gas emission by up to 65%. We further explore the market share difference in assigning optimal vehicle models by considering vehicle-based and household-based travel demand, and we found that, given currently available technology, in many cases adoption of Plug-in Hybrid Electric vehicles (PHEVs) will not necessarily result in significantly higher greenhouse gas (GHG) emissions (2.8% higher) compared to battery electric vehicles (BEVs), while PHEVs have advantages to satisfy household’s long-distance travel demand. However, if PHEV drivers never charge their vehicles, the achievable GHG emission reduction will be decreased by nearly 2/3.

Exploring motivations and barriers for long-distance trips of adult women Vermonters

Available data on long-distance travel, both quantitative and qualitative, are limited, including measures of relative accessibility to intercity destinations and its role in overall well-being. Moreover, existing travel data only include realized travel, not unmet need. Twenty-four original semi-structured interviews were conducted with women aged 25 years or older in Vermont USA to better understand the motivations and barriers of long-distance travel that cannot be fully explored in quantitative surveys. The relationship between travel, well-being, and social networks is also considered. Those interviewed indicated long-distance travel played a significant role in their lives by facilitating visits to friends and family, leisure, breaks from routine, and fulfilling personal obligations. As expected, long-distance travel was limited by cost as well as both household and work obligations. Physical limitations were noted as a barrier but the number of people raising fears about travel, being away from home, and travel logistics was unexpected. The interviews also revealed intercity travel as a symbol of social status which in turn created negative impacts for some. Latent demand for long-distance travel was common and larger than expected among the interviewees. Finally, results pointed to overlap between work and personal travel as well as a sense of obligation. While long-distance travel is often considered discretionary, this study suggests intercity non-discretionary personal trips as well as unmet demand merit future attention in both surveys and qualitative research.

Person-Based Micro-Simulation Demand Model for National Long-Distance Travel in the U.S.A.

As the nation and various states engage in funding transportation infrastructure improvements to meet future long-distance passenger travel demand, it is imperative to develop effective and practical modeling methods for analysis of long-distance passenger travel. Evaluating national-level infrastructure improvements requires a reliable analysis tool to model the demand for long-distance travel. The national travel demand model presented in this paper implements a person-level tour-based micro-simulation approach for modeling individuals’ long-distance or national activities in the U.S.A. This paper reviews the model framework, explains the model calibration, and presents applications of the model for policy evaluation and demand prediction. The model was estimated using the latest long-distance travel survey in the U.S.A., which is the 1995 American Travel Survey. As the estimation data is old, and no new long-distance travel survey with appropriate sample size is available to re-estimate the model, model calibration is the solution used to update the model and make it capable of capturing up-to-date travel patterns. Calibrating such a large-scale model can be challenging, because each calibration iteration is very costly. This paper describes the calibration effort conducted on the national long-distance micro-simulation model to showcase how a large-scale travel demand model can be calibrated efficiently. A fuel price scenario is analyzed to show how the national travel demand will change under a national fuel price increase scenario in the future year 2040. Another scenario analysis corresponding to construction of high-speed rail (HSR) is conducted to observe the effects of adding a HSR system to the northeast corridor on travel demand from a national perspective.

Long-distance and daily travel demand: integration of various travel markets and modelling approaches

Most travel demand models simplify the representation of trips from/to external zones. Despite the higher frequency of internal daily travel, long-distance travel cannot be neglected due to its high contribution to the travelled distance. In this paper we integrate two different models that represent those two travel segments. MITO is an agent-based model for travel demand implemented in the metropolitan area of Munich. DEMO is a macroscopic model that generates travel demand in Germany. DEMO is used to represent the external, long-distance travel from, to or through the MITO study area. DEMO demand is disaggregated and is jointly assigned with MITO trips using the transport model MATSim. The integration significantly improves the validation of the model against traffic counts and facilitates the simulation capabilities when the scenarios affect major roads.

통행전수화기법을 이용한 휠체어 장애인의 장거리 통행수요 추정

이 연구는 휠체어장애인의 장거리통행 서비스 개선을 위한 현 정부의 휠체어 탑승 가능 시외/고속버스의 도입을 위한 기초연구로서 휠체어장애인의 장거리통행 수요를 추정하고 이를 토대로 휠체어장애인의 장거리 통행실태를 파악하고자 한다. 구체적으로 휠체어 이용 장애인을 대상으로 장거리통행에 대한 설문조사를 실시하여 조사결과를 기초로 통행전수화기법을 이용하여 휠체어 장애인의 고속/시외버스 이용수요를 추정하였다. 분석 결과 서울－부산, 서울－대전, 대전－청주 등 대도시를 연결하는 광역통행량이 많이 나타났고, 수단별로는 기차 노선이 발달한 지역 간에는 기차 이용률이 그 외 지역에서는 자가용 이용률이 상대적으로 높게 나타났다. 통행목적별로는 친지방문이 가장 많고, 특이하게 강원권을 방문하는 장거리통행은 주로 관광목적이 많았다. 마지막으로 시간대별 통행발생률을 살펴보면, 주로 오전/오후 출퇴근시간대에 집중적으로 나타났다. 이러한 결과를 토대로 휠체어 탑승가능 버스의 노선 및 배차계획에 필요한 정보를 제공할 수 있을 것이다. 향후 연구에서는 SP조사를 통해 휠체어 장애인의 수단전환에 따른 통행변화도 함께 고려하고자 한다.

Analyzing behavioral intentions in new residential developments of motorcycle dependent cities: The case of Ho Chi Minh City, Vietnam

Many megacities within developing countries are experiencing the phenomenon of motorcycle growth. In recent decades, urban expansion has resulted in significant changes in transportation patterns including the demand for long-distance travel and the rise of car use. In addition, transit alternatives such as mass rapid transit and bus rapid transit have been introduced for improving service quality of public transport, connecting city centres and suburban areas, and tackling traffic congestion in many developing-country megacities. Therefore, the way motorcycles maintain important roles in facilitating the process of urbanisation has raised questions about future motorcycle use in new urban area. This study aims to explore behavioral intentions with regards to urban developments and new transit alternatives in motorcycle dependent cities. Ho Chi Minh City (Vietnam) is selected as a representative of developing-country megacities for this research. The study was designed using a psychological approach in order to identify corresponding factors influencing behavioral intentions. The results show that motorcycle continues to be the main mode, but less motorcycle use is also considered. Bus safety belief contributes more to the intention of public transport use in comparison with motorcycle safety awareness. Park-and-Ride scheme is likely to be a sustainable form for the combination of motorcycle and mass rapid transit in suburban areas.

Estimation of a Long-Distance Travel Demand Model using Trip Surveys, Location-Based Big Data, and Trip Planning Services

Long-distance trips are less frequent than short-distance urban trips, but contribute significantly to the total distance traveled, and thus to congestion and transport-related emissions. This paper develops a long-distance travel demand model for the province of Ontario, Canada. In this paper, long-distance demand includes non-recurrent overnight trips and daytrips longer than 40 km, as defined by the Travel Survey for Residents in Canada (TSRC). We developed a microscopic discrete choice model including trip generation, destination choice, and mode choice. The model was estimated using travel surveys, which did not provide data about destination attractiveness and modal level of service. Therefore, a data collection method was designed to obtain publicly available data from the location-based social network Foursquare and from the online trip planning service Rome2rio. In the first case, Foursquare data characterized land uses and predominant activities of the destination alternatives, by the number of user check-ins at different venue types (i.e., ski areas, outdoor or medical activities, etc.). In the second case, the use of Rome2rio data described the modal alternatives for each observed trip. Combining data from travel surveys, Foursquare, and Rome2rio, coefficients of the model were estimated econometrically. It was found that the Foursquare data on number of check-ins at destinations was statistically significant, especially for leisure trips, and improved the goodness of fit compared with models that only used population and employment. Additionally, Rome2rio mode-specific variables were found to be significant for mode choice selection, making the resulting model sensitive to changes in travel time, transit fares, or service frequencies.

The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor

Travel demand models are a useful tool to assess transportation projects. Within travel demand, long-distance trips represent a significant amount of the total vehicle-kilometers travelled, in contrast to commuting trips. Consequently, they pay a relevant role in the economic, social and environmental impacts of transportation. This paper describes the development of a microscopic long-distance travel demand model for the Province of Ontario (Canada) and analyzes the sensitivity to the implementation of a new high speed rail corridor.Trip generation, destination choice and mode choice models were developed for this research. Multinomial logit models were estimated and calibrated using the Travel Survey for Residents in Canada (TSRC). It was complemented with location-based social network data from Foursquare, improving the description of activities and diverse land uses at the destinations. Level of service of the transit network was defined by downloading trip time, frequency and fare using the planning service Rome2rio.New scenarios were generated to simulate the impacts of a new high speed rail corridor by varying rail travel times, frequencies and fares of the rail services. As a result, a significant increase of rail modal shares was measured, directly proportional to speed and frequency and inversely proportional to price.

Decision Making in an Agent-Based Simulation of Long-Distance Travel Demand

Analysis of long-distance travel demand has become more relevant in recent times due to the growing share of traffic induced by journeys related to remote activities. Consequently, there is a need of reliable long-distance travel forecasting tools like agent-based simulations. This paper presents a target-based simulation that simulates long-distance travel behavior for a long period of time. It is shown how decisions are modelled in this simulation. Activity type, duration, destination and mode are chosen simultaneously with respect to time and monetary budgets. The presented approach uses a heuristic to reduce the choice set followed by optimizing a discomfort function.

Closer to the total? Long-distance travel of French mobile phone users

Analyzing long-distance travel demand has become increasingly relevant because the share of traffic induced by journeys related to remote activities which are not part of daily life is growing. In today’s mobile world, such journeys are responsible for almost 50 percent of all traffic. Traditionally, surveys have been used to gather data needed to analyze travel demand. Due to the high response burden and memory issues, respondents are known to underreport their number of long-distance journeys. The question of the actual number of long-distance journeys therefore remains unanswered without additional data sources. This paper is the first to quantify the underreporting of long-distance tour frequencies in travel diaries. We took a sample of mobile phone billing data covering five months and compared the observed long-distance travel with the results of a national travel survey covering the same period and the same country. The comparison shows that most of the estimates of the number of missing tours by researchers have thus been too low. Our work suggests that the actual number of long-distance journeys is twice as high as that reported in surveys. Two different causes of underreporting were identified. Firstly, soft refusers travelled long distances but reported no long-distance tours. Secondly, respondents underestimated their number of long-distance tours. Consequently, there is a need to use alternative data sources in order to gain better estimates of long-distance travel demand.

Conceptual Models of the Effect of Information and Communications Technology on Long-Distance Travel Demand

Rapidly evolving information and communications technology (ICT) poses a risk to the accuracy of long-distance travel (LDT) demand forecasts. The nature of this risk is multifaceted: ICT could reduce demand if people substituted virtual for physical travel, or it could serve as a complement and thereby induce more demand. This paper presents two conceptual models for better understanding the nature of those relationships. Both models start from an existing LDT model and envision how that model can be adapted to capture ICT effects, with one considering the effects explicitly and the other implicitly. When the effects of future technology are being forecast, data collected in the past or in the present, when the future technology does not exist, are inherently limited. In such situations, conceptual models such as these can be applied by using a scenario-based approach to explore a range of reasonable outcomes. This strategy can be an important tool not just for ICT, but also for understanding the risks posed by other future technologies, such as autonomous vehicles.