Dual right-turn lanes in mitigating weaving conflicts at frontage road intersections in proximity to off-ramps

At frontage road intersections located downstream of freeway off-ramps, the use of dual right-turn lanes may provide improved weaving environments for right-turning vehicles from the off-ramp and reduce forced merges toward the desired right-turn bay. This paper investigates the safety impacts of the installation of dual right-turn lanes at frontage road intersections. A two-stage approach is used to estimate weaving conflicts as safety surrogates. In the first stage, micro-simulation models are calibrated based on field data to simulate vehicle trajectories. In the second stage, the trajectories are processed to estimate surrogate safety measures and frequency of weaving conflicts under different conditions. The two-stage approach is validated by correlation analysis between predicted weaving conflicts and actual crash rates. The results show that dual right-turn lanes can reduce weaving conflicts significantly compared to single exclusive right-turn lanes, and the safety benefits increase exponentially as weaving distance is reduced.     

Effect of built environment measures on trip distance and mode choice decision of non-workers from a city of a developing country,India

This paper investigates the influence of built environment measures on trip distance and walking decision of non-workers by segmenting the populace based on trip purpose, vehicle ownership, and the presence of school-going children in households. The built environment measures of home zone of individuals considered for the present analysis include zonal population density, zonal school enrolment, land use mix diversity index, and an indicator variable that captures if neighbourhoods have footpaths of adequate width available. Statistical analyses conducted on home-based trips indicate that an increase in the land use diversity of a zone has its strongest negative effect on distance travelled for participating in personal/household business activities. The non-vehicle owning group exhibit a higher tendency to walk than the vehicle-owning group for an increase in the land use diversity of zones. Further, the study suggests that school-enrolment in a zone also influences the travel decisions of non-workers in families with school-going children.     

Designing optimal autonomous vehicle sharing and reservation systems: A linear programming approach

Autonomous vehicle (AV) technology holds great promise for improving the efficiency of traditional vehicle sharing systems. In this paper, we investigate a new vehicle sharing system using AVs, referred to as autonomous vehicle sharing and reservation (AVSR). In such a system, travelers can request AV trips ahead of time and the AVSR system operator will optimally arrange AV pickup and delivery schedules and AV trip chains based on these requests. A linear programming model is proposed to efficiently solve for optimal solutions for AV trip chains and required fleet size through constructed AVSR networks. Case studies show that AVSR can significantly increase vehicle use rate (VUR) and consequentially reduce vehicle ownership significantly. In the meantime, it is found that the actual vehicle miles traveled (VMT) in AVSR systems is not significantly more than that of conventional taxis, despite inevitable empty hauls for vehicle relocation in AVSR systems. The results imply huge potential benefits from AVSR systems on improving mobility and sustainability of our current transportation systems.     

A Methodology for estimating traffic safety improvements at intersections

Intersection accidents represent a significant proportion of overall motor vehicle accidents. More accurate estimates of the actual effectiveness of intersection safety improvements are required. This study develops an improved methodology for post-implementation evaluation of safety countermeasures at intersections. Accidents are random, rarely occurring events. For a given time period, this leads to random fluctuations in accident frequencies, which suggests that statistical analysis employing confidence intervals, rather than point estimates, is required. Two technical problems complicate this treatment of accident occurrence as a random variable. The first problem is that identifying of hazardous locations is generally based on above-average accident frequency during the most recent period(s) for which data is available. The second problem arises from changes in external factors such as traffic volume, motor vehicle safety standards, etc., during the period of analysis, which may also affect traffic safety. A “combined” approach which addresses these technical issues is developed. Empirical Bayesian methodology is combined with regression techniques to derive a more accurate measure of the effect of safety treatments. An important consideration is the derivation of the variance of this measure, so that appropriate confidence intervals may be constructed. The approach is then applied to a sample of locations that underwent treatment by the Massachusetts Department of Public Works (MDPW). We compare our results to those which might be obtained using alternative methodologies that correct for neither or only one of the technical problems. We also illustrate how preliminary conclusions may be drawn regarding the effectiveness of broad categories of treatments, and how individual sites requiring further investigation may be identified.     

Impacts of plug-in electric vehicles in the portuguese electrical grid

Powertrain electrification is currently the best alternative to ensure sustainable energy efficient personal mobility, increasing the integration of intermittent Renewable Energy Sources (RES), improving air quality in urban centres, and reducing greenhouse gas emissions from the transport sector and their dependence on fossil energy sources. With the increasing number of Electric Vehicles (EVs) available from automotive manufacturers, one key question that arises is the capability of the electrical grid to feed the increasing energy demand of the EV fleet without major investments. This paper shows that a progressive penetration of EVs, even at a rapid rate, is perfectly possible for vehicles that offer autonomy, energy consumption and charging characteristics that are currently available in the market. This analysis is based on data acquired during a year, using a Plug-in Hybrid Electric Vehicle (PEV) as the only vehicle for a typical, Southern European Portuguese family. The energy consumption of a gasoline and electric vehicle is presented, as well as its impact on the household load pattern. An analysis of the impact on the grid is also presented, considering several penetration rates (100 thousand, 500 thousand and 1 million vehicles). As well as the avoided use of fossil fuel per vehicle and consequent reduction in overall emissions when compared with a conventional vehicle.     

Sight distance analysis of highways using GIS tools

Analyzing the distance visible to a driver on the highway is important for traffic safety, especially in maneuvers such as emergency stops, when passing another vehicle or when vehicles cross at intersections. This analysis is necessary not only in the design phase of highways, but also when they are in service. For its use in this last phase, a procedure supported by a Geographic Information System (GIS) has been implemented that determines the highway distances visible to the driver. The use of a GIS allows the sight distance analysis to be integrated with other analyses related to traffic safety, such as crash and design consistency analyses. In this way, more complete analyses could be made and costs shared. Additionally, with the procedure proposed it is possible to use data regarding the trajectory of a vehicle obtained on a highway with a Global Positioning System (GPS) device. This application is very useful when highway design data are not available. The procedure developed and its application in a case study are presented in this article.     

Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories

This paper presents a trajectory clustering method to discover spatial and temporal travel patterns in a traffic network. The study focuses on identifying spatially distinct traffic flow groups using trajectory clustering and investigating temporal traffic patterns of each spatial group. The main contribution of this paper is the development of a systematic framework for clustering and classifying vehicle trajectory data, which does not require a pre-processing step known as map-matching and directly applies to trajectory data without requiring the information on the underlying road network. The framework consists of four steps: similarity measurement, trajectory clustering, generation of cluster representative subsequences, and trajectory classification. First, we propose the use of the Longest Common Subsequence (LCS) between two vehicle trajectories as their similarity measure, assuming that the extent to which vehicles’ routes overlap indicates the level of closeness and relatedness as well as potential interactions between these vehicles. We then extend a density-based clustering algorithm, DBSCAN, to incorporate the LCS-based distance in our trajectory clustering problem. The output of the proposed clustering approach is a few spatially distinct traffic stream clusters, which together provide an informative and succinct representation of major network traffic streams. Next, we introduce the notion of Cluster Representative Subsequence (CRS), which reflects dense road segments shared by trajectories belonging to a given traffic stream cluster, and present the procedure of generating a set of CRSs by merging the pairwise LCSs via hierarchical agglomerative clustering. The CRSs are then used in the trajectory classification step to measure the similarity between a new trajectory and a cluster. The proposed framework is demonstrated using actual vehicle trajectory data collected from New York City, USA. A simple experiment was performed to illustrate the use of the proposed spatial traffic stream clustering in application areas such as network-level traffic flow pattern analysis and travel time reliability analysis.     

Vehicle purchasing behaviors comparison in two-stage choice perspective before and after eco-car promotion policy in Japan

This paper compares the vehicle purchasing behaviors in Japan between before and after the eco-car (environmental friendly vehicle) promotion policy implemented. Consumer behaviors are modeled as a two-stage decision process: a consideration set formation stage and a choice-making stage. In the first stage, all available vehicle types are included in the choice set, and consumers are assumed to apply a conjunctive screening rule to construct consideration sets. In the second stage, consumers only evaluate the vehicles in the consideration set and choose the one with maximum utility. The applied Hierarchical Bayes model can avoid the issue of an indifferentiable and irregular likelihood surface caused by thresholds and discontinuities, and the data augmentation and Markov-Chain Monte Carlo estimation methods make it possible to estimate two stages simultaneously using only the information about the consumers’ actual choices. The estimations indicate that the change of consumer behavior during the formation of consideration sets after the policy implemented: more people preferred compact and hybrid vehicles because of their better fuel efficiency and more competitive prices under the tax reduction policy. The results show, however, that most of consumers who purchase hybrid vehicles after the policy implemented are only including hybrid vehicles in their consideration sets, and oil price and vehicle price still play important roles in the choice-making stage for these who consider both gasoline and hybrid vehicles.     

使役条件下二轮电动车电池单次耐用性研究

本文通过理论和实验相结合的方法对使役条件下,在不同坡度、路面道路条件,不同充放电时间、制动次数、轮胎胎压、载重量等使用情况对二轮电动车电池的单次耐用性进行试验研究。结果发现,造成电机输出功率增加,放电电流增大的外部条件均会造成蓄电池容量减少,研究结果可指导二轮电动车车主合理科学使用二轮电动车。     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

Analysis of three independent real-world driving studies: A data driven and expert analysis approach to determining parameters affecting fuel economy

It is well established that individual variations in driving style have a significant impact on vehicle energy efficiency. The literature shows certain parameters have been linked to good fuel economy, specifically acceleration, throttle use, number of stop/starts and gear change behaviours. The primary aim of this study was to examine what driving parameters are specifically related to good fuel economy using a non-homogeneous extended data set of vehicles and drivers over real-world driving scenarios spanning two countries. The analysis presented in this paper shows how three completely independent studies looking at the same factor (i.e., the influence of driver behaviour on fuel efficiency) can be evaluated, and, despite their notable differences in location, environment, route, vehicle and drivers, can be compared on broadly similar terms. The data from the three studies were analysed in two ways; firstly, using expert analysis and the second a purely data driven approach. The various models and experts concurred that a combination of at least one factor from the each of the categories of vehicle speed, engine speed, acceleration and throttle position were required to accurately predict the impact on fuel economy. The identification of standard deviation of speed as the primary contributing factor to fuel economy, as identified by both the expert and data driven analysis, is also an important finding. Finally, this study has illustrated how various seemingly independent studies can be brought together, analysed as a whole and meaningful conclusions extracted from the combined data set.     

Site selection for EVCS in Istanbul by GIS and multi-criteria decision-making

Considering the depletion of fossil fuels and the environmental impact of transport, reducing the use of such fuels is a globally accepted priority. Thus, when moving towards alternative energy types, the first will undoubtedly be electrical energy. In order to accelerate and contribute to this process, electric vehicles (EV) must be preferred over conventional motor vehicles. However, there are various problems associated with the use of EVs, such as range and recharge status. Optimal planning of electric vehicle charging stations (EVCS) is a solution to these problems. For this purpose, parameters affecting EVCS locations have been determined in the paper. Considering these parameters and the current EVCS locations, the most suitable alternative EVCS locations were identified using Geographical Information Systems (GIS). Both the current and alternative EVCS locations were evaluated by Analytic Hierarchy Process (AHP), Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), and VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) from multi-criteria decision-making (MCDM) methods. The results show that, in Istanbul, the southeast of the European side and the southwest of the Anatolian side were most suitable for EVCS.     

Combining pickups and deliveries in vehicle routing – An assessment of carbon emission effects

This paper studies the effect on carbon emissions of consolidation of shipments on trucks. New positioning and communication technologies, as well as decision support systems for vehicle routing, enable better utilization of vehicle capacity, reduced travel distance, and thereby carbon emission reductions. We present a novel carbon emission analysis method that determines the emission savings obtained by an individual transport provider, who receives customer orders for outbound deliveries as well as pickup orders from supply locations. The transport provider can improve vehicle utilization by performing pickups and deliveries jointly instead of using separate trucks. In our model we assume that the transport provider minimizes costs by use of a tool that calculates detailed vehicle routing plans, i.e., an assignment of each transport order to a specific vehicle in the fleet, and the sequence of customer visit for each vehicle. We compare a basic set-up, in which pickups and deliveries are segregated and performed with separate vehicles, with two consolidation set-ups where pickups and deliveries may be mixed more or less freely on a single vehicle. By allowing mixing, the average vehicle load will increase and the total driven distance will decrease. To compare carbon emissions for the three set-ups, we use a carbon assessment method that uses the distance driven and the average load factor. An increase in the load factor can reduce part of the emission savings from consolidation. We find that emission savings are relatively large in case of small vehicles and for delivery and pickup locations that are relatively far from the depot. However, if a truck visits many demand and supply locations before returning to the depot, we observe negligible carbon emission decreases or even emission increases for consolidation set-ups, meaning that in such cases investing in consolidation through joint pickups and deliveries may not be effective. The results of our study will be useful for transport users and providers, policymakers, as well as vehicle routing technology vendors.     

Electric vehicles in multi-vehicle households

The suitability of an electric vehicle of a given range to serve in place of a given conventional vehicle is not limited by the daily travel over distances within that that range, but rather by the occasional inconvenience of finding alternative transport for longer trips. While the frequency of this inconvenience can be computed from usage data, the willingness of individual users to accept that replacement depends on details of available transportation alternatives and their willingness to use them. The latter can be difficult to assess. Fortunately, 65% of US households have access to the most convenient alternative possible: a second car. In this paper we describe an analysis of prospective EV acceptance and travel electrification in two-car households in the Puget Sound region. We find that EVs with 60 miles of useful range could be acceptable (i.e. incur inconvenience no more than three days each year) to nearly 90% of two-car households and electrify nearly 55% of travel in those households (32% of all travel). This compares to 120 miles range required to achieve the same fraction of electrified travel via one-for-one replacement of individual vehicles. Even though only one third of personal vehicles in the US may be replaced in this paradigm, the ‘EV as a second-car’ concept is attractive in that a significant fraction of travel can be electrified by vehicles with modest electric range and virtually no dependence on public charging infrastructure.     

Vehicle identification sensor models for origin–destination estimation

The traditional approach to origin–destination (OD) estimation based on data surveys is highly expensive. Therefore, researchers have attempted to develop reasonable low-cost approaches to estimating the OD vector, such as OD estimation based on traffic sensor data. In this estimation approach, the location problem for the sensors is critical. One type of sensor that can be used for this purpose, on which this paper focuses, is vehicle identification sensors. The information collected by these sensors that can be employed for OD estimation is discussed in this paper. We use data gathered by vehicle identification sensors that include an ID for each vehicle and the time at which the sensor detected it. Based on these data, the subset of sensors that detected a given vehicle and the order in which they detected it are available. In this paper, four location models are proposed, all of which consider the order of the sensors. The first model always yields the minimum number of sensors to ensure the uniqueness of path flows. The second model yields the maximum number of uniquely observed paths given a budget constraint on the sensors. The third model always yields the minimum number of sensors to ensure the uniqueness of OD flows. Finally, the fourth model yields the maximum number of uniquely observed OD flows given a budget constraint on the sensors. For several numerical examples, these four models were solved using the GAMS software. These numerical examples include several medium-sized examples, including an example of a real-world large-scale transportation network in Mashhad.     

Theory of safety surrogates using vehicle trajectories in macroscopic and microscopic settings: Application to dynamic message signs controlled traffic at work zones

This paper presents a new mathematical framework for obtaining quantitative safety measure using macroscopic as well as microscopic traffic data. The safety surrogate obtained from the macroscopic data is in terms of analysis performed on vehicle trajectories obtained from the macroscopic data. This method of obtaining safety measure can be used for many different types of applications. The safety surrogate for the traffic dynamics are developed in terms of a new concept of Negative Speed Differentials (NSD) that involve a convolution of vehicle speed function obtained from vehicle trajectories and then performing the integration of the square of the output for its negative values. The framework is applicable to microscopic traffic dynamics as well where we can use car following models for microscopic dynamics or the LWR model for macroscopic dynamics. This paper then presents the use of this new safety surrogate on the development of a feedback control law for controlling traffic in work zones using Dynamic Message Signs. A hybrid dynamics model is used to represent the switching dynamics due to changing DMS messages. A feedback control design for choosing those messages is presented as well as a simple simulation example to show its application.     

Home-based telecommuting and intra-household interactions in work and non-work travel: A seemingly unrelated censored regression approach

Although telecommuting has become a popular option as a new mode of working, no theoretical or empirical consensus has been reached on its potential for substituting or generating travel. This study aims to evaluate the impact of a household head’s telecommuting on household travel while controlling for the interdependence within a household and across travel purposes, by applying seemingly unrelated censored regression models to data from the 2006 Household Travel Survey in the Seoul Metropolitan Area. In terms of vehicle kilometers traveled, the analysis shows that telecommuters’ non-commute and non-work trips as well as his/her household members’ non-work trips are greater than those of non-telecommuters and their household members’, whereas telecommuting partially reduces commuting trips. However, an analysis stratified by household type reveals that the difference for household members is significant only in households with less than one vehicle per employed member: in such households (with insufficient vehicles available), the vehicle otherwise used for mandatory travel, such as for the household head’s commute, can be used for non-commute purposes or by other household members if the household head does not use it for commuting. This implies that, when vehicle travel budgets of a given household are limited, this compensatory travel mechanism can make optimum use of limited resources (i.e., vehicles), but offsets the travel-substituting effect of telecommuting. Accordingly, to more precisely estimate the impact of telecommuting-promotion policies and apply them as part of travel demand management strategies, their counteracting effects among household members should be considered.     

The cost saving potential of carsharing in a US context

Carsharing is a vehicle sharing service for those with occasional need of private transportation. Transportation planners are beginning to see great potential for carsharing in helping to create a more diversified and sustainable transport system. While it has grown quickly in the US in recent years, it is still far from the level where it can deliver significant aggregate benefits. A key element to the potential growth of carsharing is its ability to provide cost savings to those who adopt it in favor of vehicle ownership. This research seeks to quantify these potential cost savings. The costs of carsharing and vehicle ownership are compared based on actual vehicle usage patterns from a large survey of San Francisco Bay Area residents. The results of this analysis show that a significant minority of Bay Area households own a vehicle with a usage pattern that carsharing could accommodate at a lower cost. Further research is required to indentify how these cost savings translate to the adoption of carsharing.     

Comparing high-end and low-end early adopters of battery electric vehicles

Battery electric vehicle adoption research has been on going for two decades. The majority of data gathered thus far is taken from studies that sample members of the general population and not actual adopters of the vehicles. This paper presents findings from a study involving 340 adopters of battery electric vehicles. The data is used to corroborate some existing assumptions made about early adopters. The contribution of this paper, however, is the distinction between two groups of adopters. These are high-end adopters and low-end adopters. It is found that each group has a different socio-economic profile and there are also some psychographic differences. Further they have different opinions of their vehicles with high-end adopters viewing their vehicles more preferentially. The future purchase intentions of each group are explored and it is found that high-end adopters are more likely to continue with ownership of battery electric vehicles in subsequent purchases. Finally reasons for this are explored by comparing each adopter group’s opinions of their vehicles to their future purchase intentions. From this is it suggested that time to refuel and range for low-end battery electric vehicles should be improved in order to increase chances of drivers continuing with BEV ownership.     

Use of a novel dataset to explore spatial and social variations in car type,size, usage and emissions

The ’MOT’ vehicle inspection test record dataset recently released by the UK Department for Transport (DfT) provides the ability to estimate annual mileage figures for every individual light duty vehicle greater than 3 years old within Great Britain. Vehicle age, engine size and fuel type are also provided in the dataset and these allow further estimates to be made of fuel consumption, energy use, and per vehicle emissions of both air pollutants and greenhouse gases. The use of this data permits the adoption of a new vehicle-centred approach to assessing emissions and energy use in comparison to previous road-flow and national fuel consumption based approaches. The dataset also allows a spatial attribution of each vehicle to a postcode area, through the reported location of relevant vehicle testing stations. Consequently, this new vehicle data can be linked with socio-demographic data in order to determine the potential characteristics of vehicle owners.This paper provides a broad overview of the types of analyses that are made possible by these data, with a particular focus on distance driven and pollutant emissions. The intention is to demonstrate the very broad potential for this data, and to highlight where more focused analysis could be useful. The findings from the work have important implications for understanding the distributional impacts of transport related policies and targeting messaging and interventions for the reduction of car use.