Car Travel Demand Research Articles

The impact of self-driving cars on the national transport system: an assessment for Belgium

We study how full automation of the car fleet affects traffic volumes, congestion, and fuel consumption at the country level in Belgium. The central scenario in this paper looks at the combined effect of a lower opportunity cost of travel time, an increase in the acquisition price of cars by 20%, a decrease in insurance costs by 50% and a decrease in fuel consumption per km by 10%. The improvement in fuel efficiency always dominates the increase in acquisition costs, and average monetary costs decrease. Overall car travel increases by 21 up to 26%. Despite the improvement in fuel efficiency, total fuel consumption for diesel and gasoline increases by 5 up to 10%. The impact on the speed of road modes is highly location specific. A sensitivity analysis revealed that there is a threshold improvement in fuel efficiency where the “rebound effect” is nullified. To counteract the effects of full automation on total demand for car travel, a road charge close to 20 EUR cent per km would be needed.

Sustainable Transport Strategies: A Case Study of Riyad, Saudi Arabia

Transport is considered as an important action zone for achieving sustainable development since it takes a significant part in the economy sector with its existence among the production pattern, at geographic ladder [1]. Although, it is evident that the transport sector is responsible for many environmental issues. Riyad city is suffered from severe pollution caused by extensive use of motorized transport. Thus, implementation of appropriate policy tools relieve the harmful effect of transport activities by attaining sustainable transport. This paper pursues to evaluate the sustainability situation of transportation sector in Riyad city by using real data. The current paper ended with some recommendations to achieve sustainable transportation in KSA. Results indicated that the increase of car ownership and travel demand, the low use of public transport, and the rise of air and noise pollution level and the high level of road mortality lead to make the transportation system of Riyad unsustainable. On the other hand, the Saudi government have already started to execute many planned projects to achieve sustainable transportation such as the metro and bus network in Riyad and the installation of charger station of electric vehicle. In addition, the Saudi government set out many policies and target such as Target 11.2 that aims to achieve sustainable transportation in KSA and Target 3.6 that intents to minimize the high mortality rate from road accidents.

Policy implementation of multi-modal (shared) mobility: review of a supply-demand value proposition canvas

ABSTRACT Urban mobility options have increased in recent years, assisted by the widespread availability of smart device software apps, geo-positioning technology, and convenient electronic financial transactions. Multi-modal shared mobility consists of public transit systems and shared mobilities that support first/last mile travel, denoting the capability of Mobility as a Service (MaaS), and to stimulate additional non-private car travel demand. This paper reviews the supply and demand sides of implementation of multi-modal shared mobility. It found that an abundance of shared modes of car, bike, and e-scooter that are linked to public transport, can improve transport accessibility to meet specific public preferences, reduce social inequality, and minimise dilemmas from the demand side. This study introduces government policy innovations and other supporting system to improve the implementation of multi-modal shared mobility. Government policies play a key role in supporting shared mobility and technology development. However, governments do not have much information about new products such as shared mobility, which creates difficulties in subsidising multi-modal shared mobility services and potentially leads to policy failures around shared mobility schemes. This study suggests that policy entrepreneurship in collaboration with other partners, policy innovation, and the notions of merit goods and second-best policymaking can enable policy initiatives towards multi-modal shared mobility and provide supporting arguments if policies encounter failures. Implementing multi-modal shared mobility requires a collaborative partnership for a paradigm shift: service providers and government must jointly set a merit-based business model, with the support of organisations to achieve improved infrastructure provision, and smart technology applications. The findings will assist the community, business providers and government policymakers to promote multi-modal shared mobility as a pathway towards more efficient, environmentally sustainable, and socially responsive mobility solutions.

A Smart Solution to Rush-Hour Traffic Congestion: Effects of Dockless Bike-Sharing Entry on Ride-Sharing

Problem definition: Dockless bike-sharing users use a mobile app to rent a bike located throughout a city, lock the bike at their destination, and then make it available to another user. We examine whether dockless bike-sharing systems can offer a technology-enabled solution to traffic congestion problems by providing a cost-effective and sustainable alternative mode of transportation. Academic/practical relevance: Urban planners, policymakers, transportation engineers, and scholars have sought innovative and sustainable solutions to the rush-hour traffic congestion problem. Methodology: We exploit a unique natural experiment setting in which the first dockless bike-sharing service was launched in Chengdu, China, in November 2016. We employ a difference-in-differences framework and utilize detailed ride-sharing order information from DiDi Chuxing (DiDi) to measure the demand for car travel. Results: We find that the entry of dockless bike-sharing decreases the demand for car travel during rush hour. For example, the number of ride-sharing pick-ups decreases by 12 percent during rush hour after the entry of dockless bike-sharing. Moreover, more congested locations witness a greater decline in ride-sharing orders during rush hour. We also find that locations with access to public transit experience a greater decline in car travel demand, which provides empirical evidence that dockless bike-sharing helps solve the first-mile/last mile problem during rush hour. Furthermore, we find a greater reduction in car travel demand during rush hour for locations with a high level of mobility and locations that are closer to the city center. Managerial implications: Overall, the dockless bike-sharing system, which combines information technologies with a sharing economy business model, reduces car travel demand during rush hour and provides a smart solution to the rush-hour traffic congestion problem.

자율주행 공유 자동차 도입에 따른 서울시 주차수요 변화 예측

Autonomous vehicle technology, which has been rapidly developed in recent years, is expected to greatly change urban parking demand in particular when autonomous vehicles are used as shared cars. However, few studies have focused on investigating the potential parking demand changes incurred by the shared autonomous vehicles (SAVs). Under this background, this study attempts to compute the required fleet size of SAVs to provide transportation services for some portions of passenger car travel demand in Seoul and estimate the resultant parking demand changes based on a simulation approach. For this purpose, 20 scenarios are established by varying fleet size and relocation strategy of SAVs. The scenario-based analyses identify that the required SAV fleet size is 5,000 for both 3km and 5km relocation strategies when the SAV market penetration rate is 10%. The model with assumptions of 5,000 SAVs deployed and a 5km relocation strategy shows the greatest reduction in parking demand, which is equivalent to 271 vehicle-hours per day. In addition, the wider relocation strategy implies tradeoffs between operational efficiency and travel distances with no passenger. Although models with a 5km relocation strategy show greater reductions of parking demand and waiting time, they suggest travel distances with no passenger can be longer due to the relocation of SAVs over longer distances.

Marginal Cost Pricing Analysis on Tradable Credits in Traffic Engineering

This paper tries to explore a more applicable tradable credit scheme for managing network mobility from the angle of marginal cost pricing. The classic mathematical model-Cobweb model is used to analyze the stability of credit price. It is found that credit price is not always convergent in the trading market. It will show convergence, divergence, two-period simple behaviors, and even more complex dynamic behaviors, such as cycle movements and chaos. Considering the applicability and public goods character of tradable credits scheme, one public pricing mechanism- marginal cost pricing is explored. Analytical investigations and the numerical simulation of a particular case with linear demand and supply indicate that marginal cost pricing is an effective, sustainable, and socially feasible manner in managing the demand for car travel.

What Types of Cars Will We Be Driving? Methods of Forecasting Car Travel Demand by Vehicle Type

New technologies are emerging in the private vehicle market. Conventional propulsion systems are set to be replaced by alternative, more environment-friendly ones (e.g., electric vehicles), and certain new features, like autonomous driving, will possibly change the way private cars are employed. To assess the impact of such technologies, one must estimate how often and for which trips these vehicle types will be used. Another issue is the exact localization of certain vehicle types on the network, to assess environmental effects and identify where specific roadside infrastructure (e.g., charging stations) will be required. This paper presents four approaches to forecasting car usage by vehicle type using a macroscopic travel demand model in combination with a vehicle fleet or technology diffusion model. Integrating the two types of models requires tools ranging from assumptions and extrapolation of empirical data to synthetic or incremental discrete choice models. The approaches are employed in a case study forecasting travel demand using privately owned autonomous vehicles (AVs) in Germany in 2030. Despite identical input data, the estimated proportion of vehicle miles traveled (VMT) using AVs varies between 11% and 23% of overall car VMT, depending on the approach chosen. The reasons for this variation in results are investigated and some recommendations are given. To avoid the difficulties of fitting a synthetic model to observed data and to increase the accuracy of the results, the recommendation is to formulate the vehicle type choice as an incremental model added to the travel demand model.

Impact of Major Road Supply on Individual Travel Time Expenditure: An Exploration with a 30-Year Variation of Infrastructure and Travel

This study estimates the impact of major road supply on individual travel time expenditures (TTEs) using data that cover 30-year variations in transportation infrastructure and travel behavior. The impacts of the supply of road and rail infrastructure are estimated with a data set that combines records of large-scale household travel surveys in the Tokyo metropolitan area conducted in 1978, 1988, 1998, and 2008. Linear and Tobit models of individual TTEs are estimated by following the behavior of birth cohorts over the 30-year period. The models incorporate the changes in transportation infrastructure, measured as lane kilometers of two levels of major road stock and vehicle kilometers of urban rail service. The results show significant negative effects of lane kilometers for higher-level and lower-level major roads on the TTEs for all travel purposes and for commuting, after controlling for socioeconomic backgrounds and generations of individuals. This study discusses that, in Tokyo, the estimated effect is more likely to reflect the effect of a major road network per se on individual TTEs than the (indirect) effect of major road supply on individual TTEs working through land development activities (i.e., induced car travel demand). For example, the caveat is that actual road investment decisions still need to consider the induced component of road traffic in addition to the (direct) effect that is estimated in this study.

Car Travel Demand: Spillovers and Asymmetric Price Effects in a Spatial Setting

A novel analysis framework for the spatial aspects of car travel, measured by vehicle miles traveled (VMT), is introduced in this paper. The specification of a dynamic spatial Durbin model enables the analysis of VMT spatial spillovers and diffusion between neighboring areas in the short and long run. The framework is further developed to capture and introduce to a spatial setting potential asymmetry and hysteresis that can reflect reference dependence and habits. A panel data set is compiled at the subregional level, based on official car mileage recordings in England and Wales. In addition to the inelastic long-run responses of VMT to fuel price (−0.124) and income (0.116) changes, the results illustrate asymmetries and hysteresis in price elasticities with a significant spatial component. The impact magnitude on VMT from a number of factors, such as alternative fuel use, fuel deserts in rural areas, and road network and car fleet characteristics, is also estimated. The results are consistent with the car use saturation hypothesis through the positive impact of motorization rate to VMT. The negative effect of public transport infrastructure on car travel is only significant in the spatial models. The paper demonstrates the applicability and importance of spatial econometrics in transport research.

Pricing workplace parking via cash-out: Effects on modal choice and implications for transport policy

Employers often provide employees with either subsidized or free parking at work. This distorts relative prices of alternative commuting modes and produces inefficiencies in the transport market. To mitigate this price distortion, parking cash-out has been suggested as an effective and efficient policy to reduce single occupancy car commuting trips. By rewarding the abandonment of car parking use rather than penalizing continued parking, parking cash-out makes commuters sensitive to the opportunity cost of workplace parking while, at the same time, circumventing the opposition usually associated with the imposition of transport user fees. However, practical experiences are rare and only few studies investigate the effectiveness and efficiency of this policy option. This paper empirically tests the effects of parking cash-out on modal choice by performing a two-stage mode choice experiment among commuters in Germany. The first stage collects revealed commuting behavior data. The second stage proposes cash-out scenarios and observes stated mode choice behavior. The joint treatment of regular travel costs and cash outs allows comparing the traditional car travel demand cost elasticity and the parking cash-out elasticity. Results indicate that regardless of model specifications, parking cash-out has a negative and significant effect on the private car choice probability. This bears important implications for future transport policies.

Investigating inter-generational changes in activity-travel behavior: a disaggregate approach

Investigation into the long-term trends in travel behavior is crucial for strategic development of transport systems and decisions about investment in transport infrastructures. There has been a debate about if there is a constant amount of time allocated for travel and thus an upper limit to daily travel demand. Recent studies have suggested a stagnated and even declining trend of car ownership and travel demand in developed countries (the so-called ‘peak car’ and ‘peak travel’ phenomenon). This study aims at exploring the possible long-term trends in activity-travel behavior in Hong Kong to shed some light on the ‘peak travel’ debate. We have acquired two large-scale datasets from Hong Kong’s Travel Characteristics Survey conducted a decade apart and applied propensity score matching to match individuals of similar socioeconomic backgrounds from different time periods and compare their activity-travel behavior. Results indicate that households and individuals with similar socioeconomic backgrounds at the two periods have distinctively different car ownership levels and daily travel and activity behaviors.

The impact of local work and residential balance on vehicle miles traveled: A new direct approach

This paper introduces a new approach for explaining the effects of spatial competition for opportunities based on a cumulative opportunities measure of accessibility. We focus on the case of the labor market where some municipalities offer too few workplaces for their local population, forcing some of their residents to long drives. We apply this new accessibility measure to explain households' annual mileage in Switzerland using a Heckman model to account for carless households.We find that car travel demand is much greater in municipalities with a relative undersupply in the local labor market compared to a balanced or oversupplied local labor market. The results show that driving increases with greater distance to the labor market center. The model estimates allow policy makers and planners to quantify as a first assessment the expected average mileage in each municipality for new settlements and to identify municipalities with low expected annual mileage.

Fiscal instruments for regulating the sustainable development of urban transport systems in Russia

The article explains the role of public transport pricing. It proves the need for a systemic approach to building a modern public transit system. The authors argue that the main objective of the approach should be to reduce the use of private vehicles in the urban environment and increasing public transport use. It is proven that for the consumer of transport services the price per trip is an important factor when deciding whether to travel by car or by public transport. The authors analyze the available literature assessing the effects of widespread car ownership on users of the city transit system. Conflict situations that occur due to the unabated desire of city residents to travel by car are analyzed. A research method is proposed. It is shown that public transport fares have been growing in Russia at an accelerated pace when compared to the overall increase in prices of all goods and services, including motor vehicles, petrol and oils. The fare growth has resulted in a 3.6 fold drop in demand for public transport services over the 15 years being analyzed. Over the same period, the number of privately owned cars grew 120 percent. A conclusion is drawn that regular fare hikes have encouraged urban population to gradually opt against travelling by public transport. That resulted in higher demand for car travel and, eventually, in an accelerated growth in car usage. One can conclude that a persistent institutional trap has taken shape in Russian metropolises. Essentially, it means that higher public transport fares have led to lower demand for public transit services. As ridership goes down, public transport operators have to again increase prices, thus driving the demand for their services down. It is proven that escaping the trap will require restoring the ratio of prices to make sure that the price charged for a public transport trip is far lower than the cost of travelling by car. The aim of this study is to assess the influence of the factor of public transport fares on demand for private car usage and public transport ridership.

Peak car and increasing rebound: A closer look at car travel trends in Great Britain

This paper uses econometric analysis of aggregate time-series data to explore how different factors have influenced the demand for car travel in Great Britain since 1970 and how the rebound effect has changed over that time. Our results suggest that changes in income, the fuel cost of driving and the level of urbanisation largely explain travel trends over this period – with recent reductions in car travel (peak car) being driven by a combination of the rising fuel cost of driving, increased urbanisation and the economic difficulties created by the 2008 financial crisis. We find some evidence that the proportion of licensed drivers has influenced aggregate travel trends, but no evidence that growing income inequality and the diffusion of ICT technology have played a role. Our results also suggest that the rebound effect from improved fuel efficiency has averaged 26% over this period and that the magnitude of this effect has increased over time. However, methodological and data limitations constrain the level of confidence that we can have in these results.

Peak Car in Europe?

Abstract It is often argued that car travel demand has already reached a peak in developed economies. The interpretation of current trends in demographics and user preferences, as well as the increasing availability of alternative transport modes may give the impression that we have reached the age of lowering the dependence on the automobile. The factors that affect travel demand are, however, numerous and often affect car travel demand in opposing directions. The work presented here analyses the underlying factors that affect user choices and attempts to extrapolate their importance for the future across Europe. The methodology is based on the results of a recent EU-wide travel survey that maps user preferences and on the application of a Random Forest classification model that explains the interaction of the main variables that affect these choices.

Best Option For Reducing On-Campus Private Car-Based CO2 Emissions: Reducing VKT Or Congestion?

Reducing carbon footprint, especially from private cars, is an inevitable goal of sustainable transportation programs. Middle East Technical University (METU) in Ankara, Turkey, has a fairly large campus, for which carbon emissions are estimated to guide development of sustainable campus transportation demand management (TDM) policies. Focus is on reducing private car usage and increasing non-motorized and shared ride modes in a network. On the other hand, vehicle emissions with today’s engine technology are mostly governed by two major factors: Vehicle-km-travelled (VKT) and congestion, which slows down traffic and forces inefficiency in the engine performance. From engineering perspective, in order to evaluate real emission reduction potential of TDM scenarios, it is important to quantify its impact on these two factors and how much, which is the main focus of this study. For numerical examples, METU campus private car travel demand was used to calculate the VKT and speed levels for campus links by performing a network assignment in PTV-VISUM. The daily assignment was carried in three parts, as a morning and an evening peak, and an off-peak assignment, for a base case and 8 different scenarios. Emission component due to congestion was determined separately, as a percentage. The results showed that METU campus emissions were primarily function of the VKT, thus, could not be reduced by congestion management alone. They could be reduced by 30% if commuters would shift to the metro service supported by a strong in-campus shuttle services.

Guidance for transport planning and policymaking in the face of an uncertain future

Uncertainty of outcome is widely recognised as a concern facing decision-makers and their advisors. In a number of spheres of policy, it appears uncertainty has intensified in the face of globalisation, economic instability, climate change, technological innovation and changing consumer preferences. How can planners and policymakers plan for an uncertain future? There is growing interest in, and use of, techniques that can help decision-making processes where deep uncertainty is involved. This paper is based upon one of the most recent international examples of a foresight exercise employed to examine uncertainty – specifically that which concerns uncertainty over the nature and extent of future demand for car travel. The principal focus of the paper is on the insights and guidance this examination of uncertainty brings forth for transport planning and policymaking. To accommodate deep uncertainty requires a flexible and open approach in terms of how policy and investment possibilities are formulated and judged. The paper argues for a focus upon the Triple Access System of spatial proximity, physical mobility and digital connectivity as a framework for policy and investment decisions that can harness flexibility and resilience. Uncertainty becomes an opportunity for decision-makers with the realisation that they are shaping the future rather than (only) responding to a predicted future. The paper outlines two forms of policymaking pathway: regime-compliant (in which adherence to trends and the nature of the world we have known pushes policy) and regime-testing (in which the nature of the world as we have known it is brought into question and vision pulls policy decisions). Stronger orientation towards regime-testing to assist in managing an uncertain future is advocated.

Longitudinal Analysis of Car Ownership and car Travel Demand in the Paris Region using a Pseudo-panel Data Approach

We are interested in this paper to the longitudinal analysis of car ownership (number of cars per household) and car travel demand (the number of trips made by car per household and per day) in the Paris metropolitan area. The aim is to find the determinants of car ownership and use and the longitudinal analysis allow us to determine life-cycle and generation effects. Income and fuel price elasticities of demand for different residential areas and income groups are also determined. A pseudo panel data approach (which consists in grouping individuals or households into cohorts in using repeated cross-sectional data) is adopted using a succession of five large independent surveys (Enquête Globale Transport) conducted between 1976 and 2010. The cohorts of households are built from time-invariant variables. Concerning the modelling, we have estimated two models (for car ownership and car travel demand) having a semi-log linear specification. We find an elasticity of income on car ownership of 0.47. The influence of income on car ownership is decreasing with regards to a rise in income and is not significant for high income households. Moreover, the income is not a determinant of car ownership in the most urbanized area while it is positive in car dependent areas. The fuel price elasticity on car travel found is -0.22. Furthermore, the elasticity is more important in dense territories where the households can more easily adapt their behavior to a change in fuel price because alternative modes are available.

Integrating land-use planning and transportation- policy formulation in the Belfast Metropolitan Area

Much of the interest in sustainable cities relates to the inexorable rise in the demand for car travel and the contribution that certain urban forms and land-use relationships can make to reducing energy consumption. Indeed, this demand is fuelled more by increased spatial separation of homes and workplaces, shops and schools than by any rise in trip making. This paper evaluates recent efforts to integrate land-use planning and transportation policy in the Belfast Metropolitan Area by reviewing the policy formulation process at both a regional and city scale. The paper suggests that considerable progress has been made in integrating these two areas of public policy, both institutionally and conceptually. However, concerns are expressed that the rhetoric of sustainability may prove difficult to translate into implementation, leading to a further dislocation of land-use and transportation.

Urban Traffic Congestion Pricing Model with the Consideration of Carbon Emissions Cost

As one of the most effective traffic demand management opinions, congestion pricing can reduce private car travel demand and the associated carbon dioxide emissions. First, we summarized the status quo of transport carbon dioxide emission charges and congestion pricing, and then, we analyzed the characteristics of urban transport carbon dioxide emissions. Then, we proposed a (pricing) framework in which carbon emission costs would be considered as part of the generalized cost of travel. Based on this framework, this paper developed a bi-level mathematical model to optimize consumer surplus, using congestion and carbon emission charges as the control variables. A dissect search algorithm was used to solve the bi-level program model, and a numerical example was given to illustrate the methodology. This paper incorporates the emission pricing into the congestion pricing model, while considering two modes, and puts forward suitable proposals for the implementation of an urban traffic congestion pricing policy in China.