Showing papers in "Transportation Research Part A-policy and Practice in 2021"

Spatio-temporal analysis of on-demand transit: A case study of Belleville, Canada

Abstract: The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11) was substituted with the ODT providing a real-time ride-hailing service. We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019. The independent and combined effects of the demographic characteristics (population density, working-age, and median income) on the ODT trip production and attraction levels were studied using GIS and the K-means machine learning clustering algorithm. The results indicate that ODT trips demand is highest for 11:00 pm–11:45 pm during the weekdays and 8:00 pm–8:30 pm during the weekends. We expect this to be the result of users returning home from work or shopping. Results showed that 39% of the trips were found to have a waiting time of smaller than 15 min, while 28% of trips had a waiting time of 15–30 min. The dissemination areas with higher population density, lower median income, or higher working-age percentages tend to have higher ODT trip attraction levels, except for the dissemination areas that have highly attractive places like commercial areas. For the sustainable deployment of ODT services, we recommend (a) proactively relocating the empty ODT vehicles near the neighbourhoods with high level of activity, (b) dynamically updating the fleet size and location based on the anticipated changes in the spatio-temporal demand, and (c) using medium occupancy vehicles, like vans or minibuses to ensure high level of service.

Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle

Abstract: The unprecedented challenges caused by the COVID-19 pandemic demand timely action. However, due to the complex nature of policy making, a lag may exist between the time a problem is recognized and the time a policy has its impact on a system. To understand this lag and to expedite decision making, this study proposes a change point detection framework using likelihood ratio, regression structure and a Bayesian change point detection method. The objective is to quantify the time lag effect reflected in transportation systems when authorities take action in response to the COVID-19 pandemic. Using travel patterns as an indicator of policy effectiveness, the length of policy lag and magnitude of policy impacts on the road system, mass transit, and micromobility are investigated through the case studies of New York City (NYC), and Seattle—two U.S. cities significantly affected by COVID-19. The quantitative findings show that the National declaration of emergency had no policy lag while stay-at-home and reopening policies had a lead effect on mobility. The magnitude of impact largely depended on the land use and sociodemographic characteristics of the area, as well as the type of transportation system.

Mobility as a service and private car use: evidence from the Sydney MaaS trial

Abstract: Australia’s first Mobility as a Service (MaaS) trial commenced in April 2019 in Sydney, running for two years. The objective of the trial is at least twofold – to assess interest in various MaaS subscription plans through bundling public transport, rideshare, car share and car rental with varying financial discounts and monthly subscription fees, in contrast to pay as you go (PAYG); and to assess the extent to which the use of the private car might change following a subscription to a monthly mobility bundle. This paper assesses the second objective by investigating the potential for changes in monthly car use in the presence of a MaaS program. There is no previous research that we are aware of that has tested the relationship between MaaS bundle uptake and private car use. The paper develops a joint discrete-continuous model system to explain the choice between monthly bundles and PAYG, and subsequently, the total monthly car kilometres. Controlling for monthly differences due to other influences such as seasonal travel activity, the findings suggest that the offered bundles do have an encouraging impact on private car use. Within the limits of what was tested under the Sydney MaaS trial, indicative evidence suggests that MaaS has the potential to change travel behaviour in a way aligned with sustainability objectives, although this evidence should not be taken as suggesting that MaaS is a commercially viable mobility strategy.

Working from Home and its implications for Strategic Transport Modelling based on the early days of the COVID-19 pandemic

Abstract: The COVID-19 pandemic has changed the way we go about our daily lives in ways that are unlikely to return to the pre-COVID-19 levels. A key feature of the COVID-19 era is likely to be a rethink of the way we work and the implications this may have on commuting activity. Working from home (WFH) has been the ‘new normal’ during the period of lockdown, except for essential services that require commuting. In recognition of the new normal as represented by an increasing amount of WFH, this paper develops a model to identify the incidence of WFH and what impact this could have on the amount of weekly one-way commuting trips by car and public transport. Using Wave 1 of an ongoing data collection effort done at the height of the restrictions in March and April 2020 in Australia, we develop a number of days WFH ordered logit model and link it to a zero-inflated Poisson (ZIP) regression model for the number of weekly one-way commuting trips by car and public transport. Scenario analysis is undertaken to highlight the way in which WFH might change the amount of commuting activity when restrictions are relaxed to enable changing patterns of WFH and commuting. The findings will provide one reference point as we continue to undertake similar analysis at different points through time during the pandemic and after when restrictions are effectively removed.

Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships

Abstract: Ridesharing is critical for promoting transportation sustainability. As a new form of ridesharing services, ridesplitting has rarely been studied. Based on the Chicago ridesourcing trip data, this study explores how ridesplitting adoption rate (i.e., the proportion of ridesourcing trips with ridesharing authorization) varies across space and what factors are associated with these variations. We find large variations in ridesplitting adoption rates across neighborhoods (Census Tracts) and across origin–destination (Census-Tract-to-Census-Tract) pairs. Particularly, the ridesplitting adoption rate is low for airport rides. We further apply a random forest model to explore which factors are key predictors of ridesplitting adoption rate across O-D pairs and to explore their nonlinear associations. The results suggest that the socioeconomic and demographic variables collectively contribute to 68.60% of the predictive power of the model, but travel-cost variables and built-environment-related factors are also important. The most important variables associated with ridesplitting adoption are ethnic composition, median household income, education level, trip distance, and neighborhood density. We further examine the nonlinear association between neighborhood ridesplitting adoption rate and several key variables such as the percentage of white population, median household income, and neighborhood Walk Score. The revealed nonlinear patterns can help transportation professionals identify neighborhoods with the greatest potential to promote ridesplitting.

Evidence of a post-COVID change in travel behaviour – Self-reported expectations of commuting in Melbourne

Abstract: This paper addresses the question, “Will post-pandemic travel behaviour, when the virus has gone, be different to pre-pandemic travel?”. It adopts an online survey where respondents were asked to report changes in travel during the various stages of the pandemic and expectations of future travel after the virus has gone. The paper focusses on commuting including total commuting, work from home (WFH), employment, travel mode volume and share and timing of morning commute trips using reported behaviour during pandemic shutdowns; and expectations of commuting when the virus has gone. The paper provides evidence that travel behaviour post-pandemic might be different to pre-pandemic travel. It suggests that after the pandemic, public transport ridership, which declined steeply during the pandemic, will return but not to pre-pandemic levels. A post-pandemic reduction effect of around 20% in transit commuting is expected. This effect is supported using secondary evidence from a number of international cities. Results imply a mode shift from public transport use to car driving; this will be particularly large for CBD/downtown areas and is likely to result in peak period traffic congestion after the virus has gone. Work from home increased substantially during the pandemic; this will reduce after the pandemic as enforced WFH is replaced by voluntary WFH. Nevertheless, a sustained future ongoing increase in WFH above pre-pandemic levels is suggested, acting to reduce peak commuting by 6% and commuting to Melbourne CBD by 20%. However, reductions in commuting due to WFH do not offset mode shift from public transport to car driving resulting in a net increase in car use after the pandemic. Infection fear is a new top concern of public transport users since the pandemic. This fear has transitioned from ‘fresh infection fear’; the initial concerns when the pandemic started to ‘residual infection fear’; a long-term effect when the virus has gone. Implications of the findings for research and practice are discussed.

Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen

Abstract: Previous studies on the built environment and urban vitality often assume that they follow a pre-defined (mostly linear in parameters) relationship, and few studies substantiate whether high-quality transit (T) and supportive land development (D) are synergistic to vibrant urban places. This study employs gradient boosting decision trees to examine the irregularly non-linear associations between the built environment and urban vitality, using Shenzhen as the case study and the Baidu Heat Index as a proxy for vitality. It reveals that their associations change drastically past some thresholds of built environment attributes, and that there are non-linear synergies between T and D. The findings provide guidance for neighborhood planning and have implications for cities that seek transit investments and transit-oriented development.

Autonomous delivery vehicles to fight the spread of Covid-19 - How do men and women differ in their acceptance?

Abstract: Covid-19 seriously impacts and endangers lives of millions worldwide. To fight the spread of the virus, governments have taken various restricting measures including stay at home orders. Ultimately, the home delivery volume increased significantly, which still bears the risk of human–human infection during the final delivery. From a logisticians perspective, autonomous delivery vehicles (ADVs), which are a contactless delivery solution, have the potential to radically change the way groceries are delivered to customer homes and help to stop the spread of the virus. However, to date, research on user acceptance of ADVs is rare. This paper theoretically extends the Unified Theory of Acceptance and Use of Technology (UTAUT2) including gender as a moderator. The study is based on quantitative data collected in Germany through an online questionnaire (n = 501). Data were analysed using structural equation modelling. The results indicate that trust in technology, price sensitivity, innovativeness, performance expectancy, hedonic motivation, social influence, and perceived risk determine behavioural intention. However, some constructs are only significant for women. The findings of this paper have theoretical, managerial and policy contributions and implications within the areas of last-mile delivery and technology acceptance.

Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior

Abstract: This study proposes an approach for modeling travel behavior under uncertainty coupling Cumulative Prospect Theory (CPT) with Multi-attribute Decision Making (MADM) theory. CPT is utilized to depict travelers’ evaluations of each attribute, and MADM describes the process of making tradeoffs among multiple conflicting criteria. Divergent perception principles for different attributes are considered in the proposed framework. The proposed approach is utilized for an empirical analysis concerning mode shift behavior for commuting in Shanghai of China, based on data collected by stated preference surveys. Results show that the proposed approach outperforms conventional methods in terms of model performances and behavioral revelations. Empirical results demonstrate that sensitivity to gains and losses in cost and travel time are divergent in mode shift behavior. More importantly, it is found that travelers underestimate the occurrence chances of low-probability travel time and overestimate the occurrence changes of high-probability travel time in mode shift behavior, which is contrary to the findings from economics. Travelers show substantial loss aversion features as well. The heterogeneity in the value functions of CPT is investigated to shed light on differences in the evaluation process among individuals. Results reveal quite different empirical CPT parameters and behavioral mechanisms in mode shift behavior as compared to monetary experiments in economics. It highlights the importance of empirical estimations in various travel choice contexts to essentially understand travel behavior mechanisms, rather than arbitrary usage of findings from economics.

User acceptance of electric car-sharing services: The case of the Netherlands

Abstract: To reduce the externalities associated with the excessive use of carbon-fueled private vehicles, transport authorities and operators have recently been promoting one-way electric car-sharing services (ECS). Several studies attempted to identify the user acceptance and profiles of various car-sharing services, but there is a lack of consistent evidence of the psychological drivers of user acceptance. Based on an extension of the unified theory of acceptance and use of technology (UTAUT), this paper investigates the effects of six psychological constructs on behavioral intention to use ECS. Results from applying structural equation modeling to a survey with 656 respondents in the Netherlands show that social influence represents the most important driver of behavioral intention, followed by performance expectancy and personal attitude. It is also found that high satisfaction with the current means of transport for urban trips contributes to building trust in ECS companies, while car ownership has a negative indirect effect on behavioral intention. In response to the COVID-19 pandemic, the impact of hygienization measures on behavioral intention is considered. The result shows that respondents have a high degree of trust in ECS operators complying with cleaning requirements, which is translated into a lower degree of anxiety and consequently higher behavioral intention.

Quantifying economic benefits from free-floating bike-sharing systems : A trip-level inference approach and city-scale analysis

Abstract: Despite many qualitative discussions about the benefits of free-floating bike-sharing systems (FFBS), high-resolution and quantitative assessments about the economic benefits of FFBS for users are absent. This study proposes an innovative trip-level inference approach for quantifying the economic benefits of FFBS, leveraging massive FFBS transaction data, the emerging multimodal routing Application Programming Interface from online navigators and travel choice modeling. The proposed approach is able to analyze the economic benefit for every single bike-sharing trip and investigate the spatiotemporal heterogeneity in the economic benefits from FFBS. An empirical analysis in Shanghai is conducted using the proposed approach. The estimated saved travel time, cost, and economic benefit due to using FFBS per trip are estimated to be 9.95 min, 3.64 CNY, and 8.68 CNY-eq, respectively. The annual saved travel time, cost, and economic benefits from FFBS in Shanghai are estimated to be 17.665 billion min, 6.463 billion CNY, and 15.410 billion CNY-eq, respectively. The relationships between economic benefits from FFBS and built environment factors in different urban contexts are quantitatively examined using Multiple Linear Regression to explain the spatial heterogeneity in the economic benefits of FFBS. The outcomes provide a useful tool for evaluating the benefits of shared mobility systems, insights into the users’ economic benefit from using FFBS from per-trip, aggregated and spatial perspective, as well as its influencing factors. The results could efficiently support the scientific planning, operation and policy making concerning FFBS in different urban contexts.

Exploring the relationship between bike-sharing and public transport in Poznań, Poland

Abstract: It is widely believed that bike-sharing has the potential to encourage sustainable travel by combining the flexibility of cycling with the reliability of public transport However, there is actually little empirical evidence concerning the scale of that effect While many models of bike-sharing travel patterns include station locations, only a few have accounted for heterogeneity in service levels This paper aims to fill this gap by examining the case of the bike-sharing system in the city of Poznan (536,000 inhabitants) We hypothesise that a higher number of bike-sharing trips could be found in places with a higher frequency of public transport A model based on trips data mined through a web application programming interface (over 19,240,000 GPS recorded bicycle positions), and open public transport frequency data from the general transit feed specification is used Regression results show that while including control variables and spatial effects, the frequency of public transport was significantly associated with the number of bike-sharing trips A positive effect existed for short and medium trips, whereas no relationship was found for long trips Findings support the view that public transport frequency is a relevant factor for bike-sharing which should be taken into account in planning

Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity

Abstract: Understanding how built environment attributes are associated with transit commuting mode choice is essential for planners to promoting transit through land use and transportation policies. Scholars usually assume that their relationships follow a (generalized) linear pattern and are homogeneous over space. These assumptions may lead to inconsistent estimates. This study develops a semi-parametric multilevel mixed logit model to identify non-linear and spatially heterogeneous relationships between built environment attributes and transit commuting in Nanjing, China. The results show that built environment variables in residential areas have saliently non-linear associations with transit commuting, and the associations vary across traffic analysis zones. Densification facilitates transit use but it has a diminishing return. A medium level of mixed-use is conducive to transit commuting. Transit supply has to exceed a certain threshold to be effective. These findings offer nuanced guidance for transit-oriented neighborhood planning.

E-scooter sharing to serve short-distance transit trips: A Singapore case

Abstract: E-scooter sharing provides a last-mile solution to complement transit services, but less was known about its effectiveness in serving short-distance transit trips. We investigate the potential of using e-scooter sharing to replace short-distance transit trips of excessive indirectness, multiple transfers, and long access-egress walking. First, we conducted a stated preference survey on e-scooter users in the Central Area of Singapore and estimated mixed logit models to examine factors influencing the choice of e-scooters and transit. We then calculated the number of transit trips that can be replaced by e-scooters. Second, we analyzed the decision of e-scooter companies in terms of the trade-offs between serving more e-scooter trips and making more revenue under varying fares. The results show that fare, MRT transfer, and MRT access-egress walking distance have significantly negative impacts on mode utilities with random tastes among respondents. Male, young and high-income groups are more heterogeneous in e-scooter preferences compared with other groups. The loss of mode share can be nearly 17% if maximizing the revenue. We classify trade-off situations into five categories and provide suggestions of how to balance between mode share and revenue for each category. Several implications are drawn for better harnessing and regulating this new mobility service, including where to deploy e-scooters to satisfy the demand unmet by the transit and how to reach a proper balance between private operators and public welfare.

The role of objective and perceived built environments in affecting dockless bike-sharing as a feeder mode choice of metro commuting

Abstract: The rapid growth of dockless bike-sharing (DBS) systems has attracted increased academic attention in the solutions to first- and last-mile problems. However, only a few studies have examined how the synergy between DBS and metro transit is affected by objective and perceived measures of built environment collectively. This study intends to fill this research gap by focusing on the effects of objective and perceived measures of built environment on DBS–metro integrated use for commuting trips. Results reveal that low agreement between the two measures of built environment and that the perceived measure is more likely to be directly associated with DBS–metro integration than the objective measure. Different built environment attributes may affect DBS–metro integration by unique paths. Moreover, individual characteristics (i.e., gender, age, and income) and location factor moderate the association between the built environment and DBS–metro integration. Particularly, built environment attributes related to transportation service are easier to be moderated than land use and cycling condition attributes. We conclude that the understanding of and interventions for the built environment as objectively measured are necessary but not sufficient for DBS–metro integration. Promoting the perception of the built environment among different population groups is also important for interventions.

Contrasting the recommendations of participatory value evaluation and cost-benefit analysis in the context of urban mobility investments

Abstract: Participatory Value Evaluation (PVE) is a new method to assess the desirability of government projects. In a PVE, individuals select their preferred portfolio of government projects given a constrained public budget. Individuals’ preferences for (the impacts of) government projects can be determined based on these choices. The obtained preferences can be used to rank government projects in terms of their desirability. Cost-Benefit Analysis (CBA) is an alternative appraisal method used to assess the desirability of government projects. CBA establishes the desirability of public projects through analyzing people’s trade-offs between their private income and impacts of public projects. The primary objective of this paper is to investigate whether CBA and PVE lead to different policy recommendations in the context of urban mobility investments. We conducted CBAs and a PVE for 16 urban mobility investment projects and find indicative evidence that projects which focus on improving traffic safety and improvements for cyclists/pedestrians rank higher in the PVE, whereas car projects rank higher in the CBA analysis.

High-speed rail and industrial developments: Evidence from house prices and city-level GDP in China

Abstract: With unique datasets, this paper studies the distributional impact of high-speed rail (HSR) on industrial developments by examining the house price premium of industrial parks in two important core-periphery city pairs of China: Shanghai-Suzhou and Beijing-Langfang. We find that in core cities, the premium of service industrial parks (SIPs) has grown faster near HSR stations, while that of manufacturing industrial parks (MIPs) has grown slower near HSR stations. In periphery cities, however, the premium of SIPs has grown slower near HSR stations. Moreover, the premium of MIPs has grown faster near HSR stations of Suzhou. The results suggest that HSR facilitates a “spillover effect” between the core and peripheries for the manufacturing industry, but a “siphon effect” for the service industry. City-level GDP analysis for the two industries delivers consistent results. Our findings shed light on the underlying reason for the spatial variation in the economic impacts of HSR.

The slow uptake of electric cars in Italy and Slovenia. Evidence from a stated-preference survey and the role of knowledge and environmental awareness

Abstract: We report the results of a stated preference study (N = 1,934) carried out at the end of 2018 on consumers’ choices between electric cars and petrol cars in Italy and Slovenia. We estimate a hybrid mixed logit model that takes into account vehicle, infrastructure and policy attributes and two attitudinal attributes, i.e. environmental awareness and electric car knowledge. We find that purchase price and driving range play a crucial role in consumers’ decisions in both countries, whereas charging time is not statistically significant. Comparing the two countries, price sensitivity is relatively stronger in Italy, while the sensitivity for driving range and fuel economy is relatively stronger in Slovenia. Of the two latent variables we tested, we find that only environmental awareness has a statistically significant positive impact on the choice of electric cars and that it is stronger for Italians compared to Slovenians. The structural component of this latent variable indicates that women are more concerned about the environment than men, but only for the Slovenian subsample. Surprisingly, no statistically significant relationship is found between environmental awareness and age. Younger respondents are as concerned as older ones about the environment both in Italy and in Slovenia.

Revisiting the impacts of virtual mobility on travel behavior: An exploration of daily travel time expenditures

Abstract: There has been a growing interest in the association between online activities and daily activity-travel patterns. An analysis of this relationship is even more crucial considering the major disruptions to out-of-home activity participation and travel due to the COVID-19 pandemic. This study contributes to the literature by exploring the relationships between Information and Communication Technology (ICT) use (focusing on telework and online shopping) and time spent traveling using different transportation modes. Using Tobit regression models, we investigate the impacts of ICT use on three travel alternatives: (1) automobile, (2) public transit, and (3) active travel. The results show that the effects of ICT use vary across these three travel modes. For example, all else being equal, respondents with higher durations of telework tend to spend less time on auto and transit. Respondents with higher durations of online shopping spend more time walking and bicycling. This study also explores whether the effects of ICT use on travel durations vary across groups with different socio-demographics and residential location characteristics. For instance, the study finds the greater the level of land-use mixture, the stronger the association between online shopping and time spent bicycling and walking. The research findings can inform planners and decision-makers on the relationships between ICT use and overall travel behavior in order to assess travel demand under different levels of ICT use.

Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study

Abstract: On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.

Impacts of COVID-19 on the usage of public bicycle share in London

Abstract: The COVID-19 pandemic led to the adoption of many unprecedented measures to slow down the spread of the virus. Such measures have greatly impacted the entire transportation system and individuals’ travel behaviors. This paper evaluates the impacts of COVID-19 related policies, including the lockdown and the first lockdown ease on the usage of public bicycle share in London using interrupted time series approach. Our results indicate that the UK’s lockdown led to an immediate decrease in the London Cycle Hire (LCH) usage, while the first lockdown ease had no statistically significant immediate impacts. Moreover, during the lockdown period, the LCH usage showed an increasing trend and the first lockdown ease led to a much larger increase rate. Such impacts vary by the trip characteristics (i.e., occurring period and trip duration). The morning peak trips and short duration trips maintained a lower usage level during the lockdown and the lockdown ease period. On the contrary, the number of other LCH trips were much larger than that in normal days. Furthermore, the impacts on the LCH stations near the rail stations, hospitals, and parks also varied differently. The LCH trips near the rail stations reduced more after the imposition of the lockdown policy while those near the hospitals reduced less. The LCH stations near the parks had a much higher increase rate during the lockdown and the lockdown ease period than the general level. Our results provide practical implications for the policy makers and operators of the public bicycle share system.

Do policy incentives drive electric vehicle adoption? Evidence from China

Abstract: The emerging electric vehicle (EV) industry has received enormous support from global governments for its advantages in energy conservation and carbon emission reduction. This paper focuses on the development of the EV industry in China and empirically examines the impact of the government’s incentives on EV adoption in both the public and private domains using data from 61 cities spanning 2009–2018. EV-supportive policies, including financial incentives and convenience measures, are discussed, and a variable instrumental strategy is applied to solve the endogenous problem of charging facilities. The results reveal that in the public domain, financial subsidies can effectively promote the use of electric buses; however, the adoption of commercial EVs is mainly stimulated by charging facility construction and increasing fuel prices. Meanwhile, in the private domain, convenience measures, such as charging station construction and non-purchase limitations, contribute to increasing the demand for EVs, while the effect of financial incentives is not as significant as we expected.

Freight mode choice: Results from a nationwide qualitative and quantitative research effort

Abstract: The research reported in this paper focused on studying various aspects of freight mode choice in the continental United States (US) including the influencing factors, the development of econometric models to assess the impacts of public-sector policies and changes in market conditions. To gain insight into this complex subject, the team used qualitative and quantitative research techniques. The qualitative effort involved In-Depth Interviews (IDIs) with a highly selective group of leading shippers, carriers, and receivers. The IDIs provided insight into the key factors that influence mode choice, and the barriers that limit mode shifts. The quantitative effort estimated econometric models that express freight mode choice as a function of key independent variables. A unique aspect of this research is that the models were estimated using high-quality confidential data under the custody of the United States’ Census Bureau, the Internal Revenue Service, and the Surface Transportation Board, including: the Commodity Flow Survey (CFS), the largest shipper survey in the world; the Longitudinal Business Database (LBD), a comprehensive registry of commercial establishments in the US; and the Waybill Sample, a 5% sample; together with custom-made datasets of modal characteristics prepared by the authors. Using these data, the team estimated discrete-continuous freight mode choice models representing the choice of rail or truck for 42 different commodity types, and different combinations of independent variables and weighting schemes. The paper concludes with a discussion of the policy implications of the research conducted.

Infrastructure requirement for autonomous vehicle integration for future urban and suburban roads – Current practice and a case study of Melbourne, Australia

Abstract: Autonomous vehicle technology and its enabled mobility services are evolving at a more rapid pace than the understanding of the infrastructure required for them to be efficiently and safely implemented. This has not been systematically investigated in literature or practice. This research makes exploratory efforts to investigate this research area by examining and evaluating the infrastructure requirements needed to support autonomous vehicles. It formulates an infrastructure change guideline and an evaluation framework to prioritise the safety, efficiency and accessibility when integrating autonomous vehicles alongside conventional vehicles and multimodal users such as public transport commuters and pedestrians. The case study results show that for different type of regions, being a regional commercial and transportation hub in a residential area and a regional CBD street in a multimodal and spatially limited area, different arrangement and trade-offs can be made. Promisingly, the proposed guideline and framework work sufficiently, and serve as a first step towards a more systematic guideline for autonomous vehicle integration. The outcome of the research consists of a review of approaches that can guide urban planners and other users to understand and prioritise the implementation of autonomous vehicles.

Shared parking acceptance under perceived network externality and risks: Theory and evidence

Abstract: Despite the increasing interest in shared parking, little attention has been devoted to its perceptions and acceptance among drivers. To fill such a gap, this paper develops a novel theoretical framework in which perceived network externality, cost risk, safety risk, and management pressure are incorporated into the Combined Technology Acceptance Model and Theory of Planned Behavior (C-TAM-TPB). This paper merely focuses on positive network externality. First, a mathematical model is proposed to verify some of the hypotheses. The analytical results show that both a driver’s utility and the shared parking platform’s equilibrium demand are increasing in the effect of network externality and decreasing in the effect of risks. Second, a structural equation modeling approach with the partial least squares algorithm is employed for parameter estimation and model assessment. We conducted a quantitative study with 321 usable responses. The empirical results show that, except for management pressure, all the other constructs measured in the study had a significant total effect on user attitude and use intention towards shared parking. The corresponding amount of variance in attitude and use intention explained by our research model was 68.4% and 72.6%, respectively. Our findings shall provide useful insights into the better implementation of shared parking applications from managerial, technological, and operational perspectives.

Transport disrupted - Substituting public transport by bike or car under Covid 19.

Abstract: The Covid 19 pandemic has caused dramatic disruptions in the public transport sector that has seen a stark downturn in many cities across the globe, calling into question previous efforts to reduce air pollution and CO2 emissions by expanding this sector. Especially, the current surge of individual car use is worrying and the question remains which users might be able and willing to substitute public transport by cycling. This effect is interesting to study for the case of Hanover Region, because of the well-developed biking infrastructure that makes biking a viable alternative to individual car use. In this paper, we analyze survey data from June 2020 on the use of transportation modes before and during the pandemic in the Hanover Region. We ask if and how the over 4.000 participants substitute public transport and what characterizes those who chose biking over individual car use. We use multivariate regression models and find evidence that Stadtbahn (local light rail) and bus are substituted by bike, car and working from home, while train use is not significantly replaced by car and seems to be positively related to bike use. The data also shows that women have a higher level of fear of infection than men have during public transport use and therefore reduce public transport use more. Moreover, income displays a positive effect on increased car use while cycling is independent of socio-economic indicators but instead driven by the eco-consciousness of users. Surprisingly, we find that car use was increased in particular by residents of Hanover city, while it was decreased by residents of less densely populated urban areas in the region.

Why do consumers choose to buy electric vehicles? A paired data analysis of purchase intention configurations

Abstract: Understanding the driving factors associated with electric vehicle (EV) purchases is a prerequisite for governments and firms to develop corresponding policy interventions and marketing strategies. However, prior studies primarily focus on the individual role of psychological and policy attributes, and there has been limited research on how the combinations or configurations of psychological and policy attributes jointly influence consumers’ EV purchase intentions. To fill this gap, we draw on the configurational theory to develop a research framework by integrating three psychological attributes (i.e., attitude, subjective norms, and perceived behavioral control) and four policy attributes (i.e., purchase subsidies, license plate control, preferential usage, and preferential driving). We build a paired dataset with both qualitative and quantitative data. Using the fuzzy-set qualitative comparative analysis (fsQCA) approach, the empirical results from China reveal that configurations of attributes that lead to high EV purchase intention always include at least one psychological attribute. In contrast, even if a government has implemented purchase subsidies, the joint absences of attitude, subjective norms, and perceived behavioral control lead to low EV purchase intention. We also provide interesting insights into different sociodemographic characteristics. We contribute to EV adoption literature by revealing the configurations of attributes associated with EV purchase intention from a new theoretical perspective. Our findings assist policymakers in developing potential alternatives when faced with policy adjustment.

Adoption of delivery services in light of the COVID pandemic: Who and how long?

Abstract: A significant growth in demand for online shopping in light of the Coronavirus Disease (COVID) crisis has received attention from transportation practitioners, policy-makers, and researchers. However, an important question arises in this increase in online shopping and resulting deliveries: How long will this last? Very little is known whether this popularity would last a long time. To address this question, the authors conducted a survey of 915 individuals residing in the U.S. and classified them into the four distinctive consumer types (i.e., the prior adopter, temporary adopter and permanent new adopter, and non-adopter) depending on their usage of delivery services before, during, and after (expected) the COVID crisis. This research aims to gain behavioral insight by exploring the differences between the four consumer types and investigating factors affecting the initial adoption and continuance intention of using delivery services. The descriptive analysis revealed that there are clear differences not only between the four types of consumers but also between the four product types (i.e., grocery, food, home goods, and other packages) considered in the survey. The models found that factors affecting the initial adoption and continuance intention are different from the previous studies conducted before the COVID pandemic. Implications for planning and policymaking are also discussed.

Examining the influence of attitudinal factors on the use of ride-hailing services in Toronto

Abstract: The continued growth of ride-hailing usage creates the need for policymakers to understand the factors that affect the adoption and utilization of ride-hailing services. Attitudinal and perceptual factors are of particular importance, both because ride-hailing services are still evolving, and a relatively small number of studies have examined the role of these factors. This paper utilizes data from a web-based survey to understand the role that latent attitudinal factors play in adopting and using ride-hailing services in Toronto. Specifically, two binary logistic regression models are used to understand the factors that influence the adoption of exclusive and shared ride-hailing services. Besides, a zero-inflated ordered probit (ZIOP) model is estimated to investigate the factors that affect the frequency with which a person uses ride-hailing. The empirical investigation reveals that the perception of ride-hailing services tends to differ between individuals with ride-hailing experience and those without, which is expected given the relative novelty of ride-hailing. The logistic regression models reveal that, although common attributes affect the likelihood that a person has adopted a ride-hailing service, the influence of these factors varies based on the specific type of service. This underscores the value of distinguishing between exclusive and shared ride-hailing services. The ZIOP model shows that attitudinal factors regarding qualitative trip characteristics, the inclination towards using ride-hailing services in certain situations, and the consideration of parking requirements affect the frequency with which a person uses ride-hailing. Also, transit pass ownership was found to influence the frequency with which a person uses ride-hailing positively. The results of this study aim to provide insights into the adoption and utilization of ride-hailing, which can help inform policies that aim to encourage the use of shared ride-hailing as an alternative to exclusive ride-hailing services.

Why do Gender Differences in Daily Mobility Behaviours persist among workers

Abstract: Gender is commonly identified as a key explanatory factor for travel behaviour. Since women’s role in societal structure has changed in the past few decades, the question arises as to whether the “gender” factor still plays a decisive role in differences in mobility within the working population. The aim of this paper is to extend the research on gendered differences in mobility by providing an in-depth analysis of how the main determinants of daily mobility affect male and female workers differently. Unlike previous research, our econometric models included terms that express the interactions between the explanatory variables (socioeconomic variables and transport mode access) and a dichotomous gender variable, to accurately identify the marginal impact of gender on mobility indicators. Based on the Rhone-Alpes regional household travel survey (2012–2015), which includes France’s second largest urban area, the results show that even if gender differences in employment status and access to the private car are eliminated, differences in travel patterns between men and women would still be observed because the two genders do not have identical factor sensitivities. From a policy perspective, these results suggest that authorities have to adopt a gender perspective to ensure that in the future urban mobility policies provide gender equity in the context of the sustainable development of transport networks.