Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment

Emerging transportation technologies have the potential to significantly reshape the transportation systems and household vehicle ownership. Key among these transportation technologies are the autonomous vehicles, particularly when introduced in shared vehicle fleets. In this paper, we focus on the potential impact that fleets of shared autonomous vehicles might have on household vehicle ownership. To obtain initial insights into this issue, we asked a sample of university personnel and members of the American Automobile Association as to how likely they would consider relinquishing one of their household's personal vehicles if shared autonomous vehicles were available (thus reducing their household vehicle ownership level by one). For single-vehicle households, this would be relinquishing their only vehicle, and for multivehicle households (households owning two or more vehicles) this would be relinquishing just one of their vehicles. Possible responses to the question about relinquishing a household vehicle if shared autonomous vehicles are present are: extremely unlikely, unlikely, unsure, likely, and extremely likely. To determine the factors that influence this response, random parameters ordered probit models are estimated to account for the likelihood that considerable unobserved heterogeneity is likely to be present in the data. The findings show that a wide range of socioeconomic factors affects people's likelihood of vehicle relinquishment in the presence of shared autonomous vehicles. Key among these are gender effects, generational elements, commuting patterns, and respondents' vehicle crash history and experiences. While people's opinions of shared autonomous vehicles are evolving with the continual introduction of new autonomous vehicle technologies and shifting travel behavior, the results of this study provide important initial insights into the likely effects of shared autonomous vehicles on household vehicle ownership.     

Demand for electric vehicles in hybrid households: an exploratory analysis

Previous studies of the potential market for battery electric vehicles (BEVs) from different research streams have failed to converge on a single, robust estimate. What these previous research streams share are untested or implausible assumptions about consumer response to new transportation technology. We frame the BEV purchase decision in terms of a household's entire stock of vehicles, car purchase behavior and travel behavior. Within this framework, households which own both electric vehicles and gasoline vehicles are called ‘hybrid households’. Because nearly all consumers are unfamiliar with the characteristics of BEVs, we designed an interactive interview based on week-long travel diaries, which we call Purchase Intentions and Range Estimation Games (PIREG) to explore hypothetical hybrid household vehicle use. Our primary finding is that consumers' perceived driving range needs are substantially lower than previous hypothetical stated preference studies conclude. We find evidence of a viable market for BEVs with 60 to 100 miles driving range.     

Logistics facilities for intra and inter-regional shipping: Spatial distributions,location choice factors,and externality

Existing studies of urban logistics facility locations and their impacts on negative externality tend to focus on the movements of freight that start and end within a metropolitan area and overlook the role of the shipments with origin or destination outside of the urban area – external shipments – in characterizing logistics facilities. This research aims to fill such gap. Focusing on the Tokyo Metropolitan Area, we analyze locations and goods vehicle trips associated with four logistics facility groups that have different splits between intra and inter-regional trips for inbound and outbound shipments. The spatial density analysis and the location choice modeling highlight the differences in the distribution and location choice factors among those groups. The importance of trip distance minimization is more evident for the facilities that serve intra-regional shipments. Furthermore, we show that the traffic impacts of goods vehicle traffic associated with logistics facilities also differ in many ways among the above-mentioned groups. Finally, we discuss the policy insights to address their heterogeneity in the contribution to externality.     

Location factors for logistics facilities: Location choice modeling considering activity categories

While many past studies analyze the changes in the spatial distribution of logistics facilities and provide insights on the factors that drive the migration of logistics facilities, only a few studies analyze the factors of location choice at the individual facility level. Furthermore, the differences among various facility types, with respect to the key locational characteristics that attract logistics facility developments, are still not clear. This research attempts to fill the research gap by analyzing the location choices for logistics facility development. Using the data from the Paris Region, we characterize the locations and activities of logistics facilities and estimate a logistics facility location choice model for each activity category. The analysis reveals the key locational characteristics that influence logistics facility locations, such as zoning regulations, wholesale job accessibility, population density, and the accessibility to autoroute (controlled-access highway), as well as the heterogeneity in the effects of these characteristics by activity category. Zoning and traditional clusters play a significant role for the locations of newly developed logistics facilities, which underlines the importance of public policies for logistics land use in the Paris region. The effect of the accessibility to population is identified for the group of facilities which serve for retail shops and end-consumers only under the assumption that alternative locations are limited to specialized economic activity/logistics zones, indicating that population accessibility is a secondary factor to the designation as the specialized zone. Zoning changes to/from economic activity/logistics zones may cause significant impacts on the spatial distribution of logistics facilities, and thus, the distribution of goods vehicle traffic flow.     

Traffic barriers and pedestrian crossing behaviour

This paper is concerned with the relationships between traffic conditions and pedestrian behaviour which determine the extent of barrier effects. These relationships are examined on a high-density mixed-use street in central Edinburgh. Such streets are generally important routes for both pedestrians and vehicles, where there is maximum potential for conflicts between them. Crossing strategies and pedestrian trip-making activity are shown to be modified in response to changes in traffic conditions, notably to changes in traffic volumes and parking activity. Barrier effects are seen to vary markedly with the age group of pedestrians. The findings are related to pedestrian safety on such streets and to issues involved in the design and implementation of traffic management schemes. It is concluded that barrier effects on pedestrians should be a standard consideration in scheme design and implementation.     

New mobility service users' perceptions on electric vehicle adoption

The recent growth of new mobility services such as car-sharing (ZipCar, Car2Go) and ride-hailing (Uber, Lyft) has interesting implications for new vehicle technologies. We explore the users of the services and their relation to electric vehicles preferences by analyzing two large-scale mobility service surveys. A number of categories (car-share usage, ride-hail usage, commute mode, demographics, current vehicle attributes, environmental attitudes, technology attitudes, and life-stage information) are examined in order to determine the likelihood a respondent considers purchasing an electric vehicle in the future. Survey respondents explicitly expressed that exposure to ride-hailing did not increase their propensity for wanting to purchase an electric vehicle in the future. However, we run a full suite of cross-validation models and find that in addition to the typical factors used in modeling preferences, the use of new mobility services statistically increases the predictive power of our model to identify preferences for electric vehicles.     

An international comparison of the self-reported causes of cyclist stress using quasi-naturalistic cycling

This study explores the influences of attitudes and setting on cyclists' stated causes of stress using survey techniques and quasi-naturalistic cycling in both Delft, The Netherlands and Atlanta, Georgia, USA. The study recruited 28 participants in Delft and 41 in Atlanta. Participants cycled approximately 30 min on specified routes in both cities on an instrumented bicycle. Prior to cycling, the participants filled in a written survey about their cycling habits, attitudes, and demographics. At specified points during and after the ride, participants were interviewed about their stress levels throughout the ride and the causes of those stress levels. Thematic analysis and statistical methods are used to understand the interactions of setting (country), attitudes, stated stress, and sensor data. The top three stressors were motor vehicles, pavement, and poor infrastructure; 83% of participants mentioned a motor vehicle causing stress, 64% mentioned road surface, and 58% mentioned infrastructure. The results confirm the importance of motor vehicle interaction to cyclist stress, but also highlight some new insights on stress such as the importance of pavement condition. Speed differentials between cyclists and vehicles were also shown to be important and suggested cyclists in Delft felt comfortable to travel their ideal speed. This speed preference was supported by GPS data that showed the cyclists in Delft were cycling at speeds about half (12 kph) that of the cyclists in Atlanta (24 kph). Review of close-pass events demonstrated that cyclists in Delft were more comfortable with closer passes which could be associated with their belief that motorists notice them and/or speed differences between the vehicle and bicycle. The results also suggest that number of vehicle travel lanes can have mixed impacts on cyclist stress. These findings can be taken into consideration when designing bicycle facilities to create low-stress cycling networks.     

Infrastructure development for alternative fuel vehicles on a highway road system

A new mathematical model for positioning alternative fuel (AF) refueling stations on directed-transportation networks with the objective of maximizing the coverage of path flow volume is proposed. This model is especially designed for developing an AF infrastructure on toll roads and other highways, where vehicles do not need to exit the road network for refueling, some candidate station locations are not located at interchanges, and some stations may only service vehicles on one driving direction. The proposed model is applied to the Pennsylvania Turnpike System using the 2011 truck traffic data and considering different vehicle driving ranges.     

Employment centers and travel behavior: exploring the work commute of Mumbai’s rapidly motorizing middle class

In the Greater Mumbai Region (GMR), jobs and housing are agglomerating in nodes in the periphery of Mumbai City. However, current transportation investments focus on strengthening connections within Mumbai City, while these outlying nodes have received less attention. As housing and jobs move out, given limited travel choices, the need for mobility nudges many middle class Indian households into owning private vehicles. Using household travel survey data from the GMR, this paper develops an understanding of how worker’s trips are different for those who commute to the city versus the exurbs. Socio-economic and transportation indicators for middle class workers going to the city versus the exurbs show that these populations are quite similar demographically. However, those traveling to the exurbs, on average, tend to be at a socio-economic disadvantage with respect to income, education and out-of-pocket travel burdens. Those traveling to exurban work locations have shorter travel times and trip distances, and make much higher use of walking, biking, rickshaws, and motorized two-wheelers compared to commuters to Mumbai City. Across the GMR, car users travel longer and farther compared to motorized two-wheeler users. On average, traveling by a private vehicle is faster than bus or rickshaw travel revealing advantages of private vehicle use. These mode choices in the middle class have resulted in rapid motorization and negative externalities such as traffic congestion and emissions. Evidence of large increases in motorized two-wheelers and cars in India suggests that these modes will likely keep growing, unless competing efficient travel options are supplied.     

Evaluation of land-use and transport network effects on cyclists' route choices in the Copenhagen Region in value-of-distance space

Growing interest in sustainable transportation systems has driven decision-makers toward policies and investments aimed at promoting cycling, but little to no effort has been made toward incorporating bicycle transport in transport planning models. This study contributes toward this direction by estimating a bicycle route choice model in value-of-distance space from a large sample of 3384 cycling trips that were traced with GPS devices in the Copenhagen Region. The novelty of this study lies in (i) observing cyclists' behavior in a cycling-oriented country, (ii) exploiting rich data about the cycling environment, (iii) estimating the model in value-of-distance rather than preference space, and (iv) not focusing only on preferences for traditional variables (e.g., distance, turns, hilliness, intersections, motorized road characteristics), but also on perceptions and preferences for bicycle facilities (e.g., bicycle lanes, bicycle paths, bicycle traces) and land-use designations (e.g., residential, industrial, sports, scenic areas). The findings from the model show that: (i) cyclists exhibit heterogeneous preferences for avoiding right and left turns, cycling the wrong way, using roundabouts and bridges, and cycling alongside residential and scenic areas; (ii) cyclists dislike cycling on unpaved and hilly surfaces and alongside larger roads; (iii) cyclists have clear perceptions about different types of bicycle facilities, with a preference for bicycle lanes and segregated paths; (iv) cyclists have clear perceptions about land-use designations, with a preference for cycling alongside sports and scenic areas; (v) time-of-day and air temperature contribute to the perceptions of cyclists and their preferences for bicycle facilities and land-use designations.     

Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.     

Future mobility in India: Implications for energy demand and CO2 emission

The aim of this study is threefold. First, it aims to provide a reliable data set of land-based passenger traffic volumes in India from 1950–1951 to 2000–2001 for the five major motorized modes of transport—two-wheelers, cars, auto-rickshaws, buses, and railways. Second, based on this data set, it aims to estimate the long-term trends in motorized traffic volume and modal split up to the year 2020–2021. Third, based on the projected values of aggregate traffic volume and modal split, this study aims to estimate the level and growth of energy demand and CO₂ emission from the passenger transport sector in India. It is found that the motorized traffic volume in India will very nearly touch the mark of 13 000 billion passenger-kilometers in 2020–2021, out of which 91.7% will be provided by the roads and the rest by railways. If there is no reduction in modal energy and CO₂ intensities, energy demand is projected to increase from 1060.8 peta joules in 2000–2001 to 5584.4 peta joules in 2020–2021 and CO₂ emission will increase from 19.80 to 93.25 million metric tons of carbon equivalent during the same period.     

Locating road-vehicle refueling stations

This study follows the concept of set cover for proposing a refueling-station-location model using a mixed integer programming method, based on vehicle-routing logics. Its solution uses only the easy-obtain data of the origin-destination distance matrix. A case study that focuses on the siting of refueling stations for achieving multiple origin-destination intercity travel via electric vehicles on Taiwan demonstrates the applicability of the model. Sensitivity analysis shows that greater vehicle range will result in a lower number of refueling stations that need to be sited. Range is crucial in reducing the facility-location costs, and therefore is an important issue in the development of alternative-fuel-vehicle technology.     

A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.).     

Latent heterogeneity in autonomous driving preferences and in-vehicle activities by travel distance

Autonomous driving technologies (ADTs) are transformative because they are expected to assume the task of driving. Since travel distance is closely related to the burden of driving, it is expected to affect the ADT preference; however, this effect has not been investigated in the transportation literature. Therefore, this study is designed to investigate the ADT preference and heterogeneity with respect to different travel distances. Hypothetical choice situations were designed to investigate people's ADT preferences over human driving for varying travel distances. Using the stated preference data collected via the experiments, four latent class models were estimated for heterogeneity across classes and travel distances. The estimation results show that three classes, we labeled as competitive, autonomous vehicle oriented, human-driven vehicle oriented, are revealed by the ADT preference, regardless of the travel distance. In addition, in-vehicle activities in hypothetical autonomous driving situations were observed across all the classes and travel distances.     

Using triangulation to assess a suite of tools to measure community severance

There is a lack of tools to identify and measure community severance caused by large roads and motorised traffic, despite the evidence of its negative impacts on local communities. This paper reports the development of a suite of tools to measure and value community severance, undertaken as a part of the Street Mobility and Network Accessibility research project. The tools include participatory mapping, spatial analysis, a video survey, street audits, a health and neighbourhood mobility survey, and a valuation tool based on a stated preference survey. They were tested in the area around Finchley Road, a busy arterial road in North London. The study found that Finchley Road is an unpleasant place for pedestrians due to high traffic levels, air and noise pollution, and the lack and poor quality of pedestrian crossing facilities. This has a negative impact on the mobility and accessibility of local residents and, to some extent, on their health and wellbeing. The analysis showed coherence between the findings from the different measurement tools applied individually, but also revealed interconnections between factors which contribute to severance, demonstrating that overall the suite is reliable for assessing community severance in urban areas. Overall, the paper provides a multidisciplinary approach to developing standardised methods to measure a negative impact of transport that is still relatively unknown.     

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.     

An optimal approach for a set covering version of the refueling-station location problem and its application to a diffusion model

This article deals with the refueling-station location problem for alternative fuel vehicles in a traffic network. Alternative fuel vehicles can be characterized by the vehicle range that limits the travelable distance with fuel at full capacity. I propose an efficient formulation of the refueling-station location problem using an optimal property and prove that the problem is NP(Non-deterministic Polynomial)-complete in the strong sense. I consider a special case of the refueling-station location problem in which the construction costs are equal for all nodes. In this case, the problem is to determine refueling station locations to minimize the total number of stations, while making the possible multiple predetermined origin–destination round-trips. I propose an optimal algorithm applicable when no refueling stations currently exist in a traffic network and a dynamic programming based algorithm applicable when a set of refueling stations already exists. I apply the algorithms to a traffic network to study the diffusion of refueling stations and predict the speed and range of station establishment. The computational experiments show that the speed of diffusion depends on the vehicle range and the sequence of the origin–destination demands considered in the diffusion process.