Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.     

Finding time‐robust fuel‐efficient paths for a call‐taxi in a stochastic city road network

Intra‐city commuting is being revolutionized by call‐taxi services in many developing countries such as India. A customer requests a taxi via phone, and it arrives at the right time and at the right location for the pick‐up. This mode of intra‐city travel has become one of the most reliable and convenient modes of transportation for customers traveling for business and non‐business purposes. The increased number of vehicles on city roads and raising fuel costs has prompted a new type of transportation logistics problem of finding a fuel‐efficient and quickest path for a call‐taxi through a city road network, where the travel times are stochastic. The stochastic travel time of the road network is induced by obstacles such as the traffic signals and intersections. The delay and additional fuel consumption at each of these obstacles are calculated that are later imputed to the total travel time and fuel consumption of a path. A Monte‐Carlo simulation‐based approach is proposed to identify unique fuel‐efficient paths between two locations in a city road network where each obstacle has a delay distribution. A multi‐criteria score is then assigned to each unique path based on the probability that the path is fuel efficient, the average travel time of the path and the coefficient of variation of the travel times of the path. Copyright © 2016 John Wiley & Sons, Ltd.     

Design of an intersection decision support (IDS) interface to reduce crashes at rural stop-controlled intersections

Rural, stop-controlled intersections pose a crash risk to drivers, particularly elderly drivers. This paper outlines the design phase of an infrastructure-based intersection decision support (IDS) system to help drivers make safer gap acceptance decisions at rural intersections. A human factors-based design process was conducted to determine the type of information that should be presented to drivers. Information considered important for presentation to the driver included showing the presence of gaps, indicating the size of available gaps, and/or judging the safety of available gaps. This paper discusses the process used to determine the appropriate design specifications for initial testing of the IDS system interface.     

Bicyclist commuters’ choice of on-street versus off-street route segments

When using limited funds on bicycle facilities, it would be helpful to know the extent to which a new facility will be used. If a bicycle lane is added to a street, how many bicyclists will no longer use the adjacent sidewalk? If a separate bicycle path is constructed, how many bicyclists will move from the street or sidewalk? This study seeks to identify factors that explain a bicyclist’s choice between available facility choices—off-street (sidewalk and bicycle path) or on-street (bicycle lane and roadway). This paper investigates these issues through a survey of bicyclists headed to Purdue University in West Lafayette, IN, USA. The first data collected to address these questions were “site-based”. Bicyclists were interviewed on campus at the end of their trips and asked which part of the cross-sections along their routes they had used—on-street or off-street. The characteristics of a particular cross-section of street right-of-way were then compared against the characteristics of each bicyclist and his/her observed choice of street, sidewalk, lane, or path. Later, “route-based” serial data were also added. The study developed a mixed logit model to analyze the bicyclists’ facility preferences and capture the unobserved heterogeneity across the population. Effective sidewalk width, traffic signals, segment length, road functional class, street pavement condition, and one-way street configuration were found to be statistically significant. A bicycle path is found to be more attractive than a bicycle lane. Predictions from the model can indicate where investments in particular bicycle facilities would have the most desirable response from bicyclists.     

Roadway determinants of bicyclist exposure to volatile organic compounds and carbon monoxide

Few studies have quantified relationships between bicyclist exposure to air pollution and roadway and traffic variables. As a result, transportation professionals are unable to easily estimate exposure differences among bicycle routes for network planning, design, and analysis. This paper estimates the effects of roadway and travel characteristics on bicyclist exposure concentrations, controlling for meteorology and background conditions. Concentrations of volatile organic compounds (VOC) and carbon monoxide (CO) are modeled using high-resolution data collected on-road. Results indicate that average daily traffic (ADT) provides a parsimonious way to characterize the impact of roadway characteristics on bicyclists’ exposure. VOC and CO exposure increase by approximately 2% per 1000 ADT, robust to different regression model specifications. Exposure on off-street facilities is higher than at a park, but lower than on-street riding – with the exception of a path through an industrial corridor with significantly higher exposure. VOC exposure is 20% higher near intersections. Traffic, roadway, and travel variables have more explanatory power in the VOC models than the CO model. The quantifications in this paper enable calculation of expected exposure differences among travel paths for planning and routing applications. The findings also have policy and design implications to reduce bicyclists’ exposure. Separation between bicyclists and motor vehicle traffic is a necessary but not sufficient condition to reduce exposure concentrations; off-street paths are not always low-exposure facilities.     

Delay to major street through vehicles at two-way stop-controlled intersections

This paper describes a methodology for predicting the delay to major street through vehicles at two-way stop-controlled intersections. This delay is incurred when major street left-turn demand exceeds the available storage area and blocks the adjacent through lane. The through lane blockage problem does not generally occur with significant frequency on streets with divided cross sections that have left-turn bays or lanes; however, it frequently occurs on undivided streets due to their lack of left-turn storage. To minimize this delay, through drivers often merge with vehicles in the adjacent through lane—if there is an adequate gap for them to safely merge into. If there is no merge opportunity, then the through drivers will stay in the inside lane until the queue ahead dissipates. The through vehicle delay predicted by the methodology was found to be relatively small (i.e. less than 5 s veh⁻¹) when compared with delays commonly incurred by non-priority movements at unsignalized intersections. However, when expressed in terms of total vehicle hours of delay, the effect can be quite significant. In general, through vehicle delay increases with increasing approach flow rate and left-turn percentage. However, at flow rates in excess of about 1400 veh h⁻¹, delays increase very rapidly and there is evidence that larger left-turn percentages may have lower delays. ©     

Safety in the road environment: a driver behavioural response perspective

The existing literature on road safety suggests that a driver's perception of safety is an important influence on their driving behaviour. A challenging research question is how to measure the perception of safety given the complex interactions among drivers, vehicles and the road setting. In this paper, we investigate a sample of driver evaluations of the perception of safety associated with a set of typical road environments. A roundabout was selected as the context for the empirical study. Data was obtained by a computerised survey using the video-captured road and traffic situations. A controlled experiment elicited driver responses when faced with a mixture of attributes that describe the roundabout environment. An ordered probit model identified the contribution of each attribute to the overall determination of the perception of safety. An indicator of perceived safety was developed for a number of typical road and traffic situations and for different driver segments.     

Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging.     

We can all get along: The alignment of driver and bicyclist roadway design preferences in the San Francisco Bay Area

Two trends in the United States—growth in bicycling and enthusiasm for complete streets—suggest a need to understand how various roadway users view roadway designs meant to accommodate multiple modes. While many studies have examined bicyclists’ roadway design preferences, there has been little investigation into the opinions of non-bicyclists who might bicycle in the future. Additionally, little research has explored the preferences of the motorists who share roads with cyclists—despite the fact that motorists compose the vast majority of roadway users in the United States and similarly developed countries.This paper presents results from an internet survey examining perceived comfort while driving and bicycling on various roadways among 265 non-bicycling drivers, bicycling drivers, and non-driving bicyclists in the San Francisco Bay Area. Analysis of variance tests revealed that both drivers and bicyclists are more comfortable on roadways with separated bicycling facilities than those with shared space. In particular, roadways with barrier-separated bicycle lanes were the most popular among all groups, regardless of bicycling frequency. Striped bicycle lanes, a common treatment in the United States, received mixed reviews: a majority of the sample believed that they benefit cyclists and drivers through predictability and legitimacy on the roadway, but the lanes were rated significantly less comfortable than barrier-separated treatments—particularly among potential bicyclists.These findings corroborate research on bicyclists’ preferences for roadway design and contribute a new understanding of motorists’ preferences. They also support the U.S. Federal Highway Administration’s efforts to encourage greater accommodation of bicyclists on urban streets.     

Age related annual crash incidence rate ratios in professional drivers of heavy goods vehicles

ObjectivesEvidence concerning crash risk for older heavy vehicle drivers is sparse, making it difficult to assess if it is prudent to encourage older drivers to remain in the workforce in a climate of labour shortages. The objective of this study was to estimate annual crash rate ratios of older male heavy vehicle drivers relative to their middle aged peers.MethodsData utilized in this study includes all crashes meeting inclusion criteria involving heavy goods vehicles, categorised as rigid trucks and articulated trucks; this data was recorded by the New South Wales Roads and Traffic Authority. The exposure to the risk of a crash was represented by distance travelled for each vehicle type and year, by age of driver, as estimated by the Australian Survey of Motor Vehicle Use. Negative binomial regression modelling was applied to estimate annual crash incidence rate ratios for male drivers in various age groups.ResultsA total of 26,146 crashes occurred in New South Wales during 1999–2006, involving a total of 54,191 vehicles; removing observations that did not meet the inclusion criteria, 19,736 observations remained representing 12,501 crashes. For rigid trucks, the incidence rate ratio for drivers aged 65+ years, compared to 45–54 year olds, was 0.74 (95% CI 0.51, 0.98). For articulated trucks, the annual crash incidence rate ratio for drivers aged 65+ years compared to 45–54 year olds was 1.4 (95% CI 0.96, 1.9), and that for drivers aged 55–64 years compared to 45–54 year olds was 1.1 (95% CI 0.83, 1.3).ConclusionsOlder male professional drivers of heavy goods vehicles have lower risk of crashes in rigid vehicles, possibly due to accrued driving experience and self-selection of healthy individuals remaining in the workforce. Thus, encouraging these drivers to remain in the workforce is appropriate in the climate of labour shortages, as this study provides evidence that to do so would not endanger road safety.     

Efficient solution algorithm for finding spatially dependent reliable shortest path in road networks

Travel times are generally stochastic and spatially correlated in congested road networks. However, very few existing route guidance systems (RGS) can provide reliable guidance services to aid travellers planning their trips with taking account explicitly travel time reliability constraint. This study aims to develop such a RGS with particular consideration of travellers' concern on travel time reliability in congested road networks with uncertainty. In this study, the spatially dependent reliable shortest path problem (SD‐RSPP) is formulated as a multi‐criteria shortest path‐finding problem in road networks with correlated link travel times. Three effective dominance conditions are established for links with different levels of travel time correlations. An efficient algorithm is proposed to solve SD‐RSPP by adaptively using three established dominance conditions. The complexities of road networks in reality are also explicitly considered. To demonstrate the applicability of proposed algorithm, a comprehensive case study is carried out in Hong Kong. The results of case study show that the proposed solution algorithm is robust to take account of travellers' multiple routing criteria. Computational results demonstrate that the proposed solution algorithm can determine the reliable shortest path on real‐time basis for large‐scale road networks. Copyright © 2016 John Wiley & Sons, Ltd.     

Implementing the maximum likelihood methodology to measure a driver’s critical gap

Most of the capacity calculation procedures for two-way stop-controlled (TWSC) intersections are based on gap acceptance models. Critical gap is one of the major parameters for gap acceptance models. The accuracy of capacity estimation is mainly determined by the accuracy of the critical gap. This paper focuses on the implementation of the maximum likelihood technique to measure a driver’s critical gap using field data. A methodology to define gap events is proposed, so that the accepted gaps and maximum rejected gaps required by the maximum likelihood technique could be obtained. Specific issues regarding multi-lane situations and major street right turn movement are discussed. Special conditions observed during the research are addressed when the proposed method cannot be applied directly, such as the existence of a mid-block refuge area where minor street drivers can seek gaps in a two-stage process, pedestrian blockage, and downstream queue spill back. The proposed method was adopted in measuring critical gap under US conditions during a research project, described by Kyte et al. (1996). ©     

An investigation of the reliability of real-time information for route choice decisions in a congested traffic system

This paper investigates the reliability of information on prevailing trip times on the links of a network as a basis for route choice decisions by individual drivers. It considers a type of information strategy in which no attempt is made by some central controller or coordinating entity to predict what the travel times on each link would be by the time it is reached by a driver that is presently at a given location. A specially modified model combining traffic simulation and path assignment capabilities is used to analyze the reliability of the real-time information supplied to the drivers. This is accomplished by comparing the supplied travel times (at the link and path levels) to the actual trip times experienced in the network after the information has been given. In addition, the quality of the decisions made by drivers on the basis of this information (under alternative path switching rules) is evaluated ex-post by comparing the actually experienced travel time (given the decision made) to the time that the driver would have experienced without the real-time information. Results of a series of simulation experiments under recurrent congestion conditions are discussed, illustrating the interactions between information reliability and user response.     

Proposed collision warning system for right-turning vehicles at two-way stop-controlled rural intersections

At two-way stop-controlled (TWSC) rural intersections, a right-turning driver who is departing the minor road may select an improper gap and subsequently may be involved in a rear-end collision with another vehicle approaching on the rightmost lane on the major road. This paper provides perceptual framework and algorithm design of a proposed infrastructure-based collision warning system that has the potential to aid unprotected right-turning drivers at TWSC rural intersections. The proposed system utilizes a radar sensor that measures the location, speed, and acceleration of the approaching vehicle on the major road. Based on these measurements, the system’s algorithm determines if there will be any potential conflict between the approaching and the turning vehicles and warns the driver of the latter vehicle if such a conflict is found. The algorithm is based on realistic acceleration profile of the turning vehicle to estimate its acceleration rates at different times so that the system can accurately estimate the time and distance needed for the departing vehicle to accelerate to the same speed as for the approaching vehicle. That realistic acceleration profile is established using actual experimental data collected by a Global Positioning System (GPS) data logger device that was used to record the positions and instantaneous speeds of different right-turning vehicles at 1-s intervals. The algorithm also gives consideration to the time needed by the driver of the departing vehicle to perceive the message displayed by the system and react to it (to start departure) where it was found that 95% of drivers have a perception–reaction time of 1.89 s or less. A methodology is also illustrated to select the maximum measurement errors suggested for the detectors in measuring the locations of the approaching vehicle on the major road where it was found that the accuracy of the system significantly deteriorates if the errors in measuring the distance and the azimuth angle exceed 0.1 m and 0.2°, respectively. An application example is provided to illustrate the algorithm used by the proposed system.     

Effects of intersection field of view on emergent collision avoidance performance at unsignalized intersections: analysis based on driving simulator experiments

Proper intersection sight distance can effectively lower the possibility of intersection accidents. American Association of State Highway and Transportation Officials (2011) provide a series of recommended dimensions of intersection sight triangles for uncontrolled and stop/yield‐controlled intersections. However, in reality, although the actual intersection design for unsignalized intersections satisfies the requirements of sight distance and clear sight triangle in American Association of State Highway and Transportation Officials' guideline, there are still a large number of crashes occurring at unsignalized intersections for drivers running stop/yield signs or failing to slow down. This paper presents a driving simulator study on pre‐crash at intersections under three intersection field of view (IFOV) conditions. The aim was to explore whether better IFOVs at unsignalized intersections improve their emergent collision avoidance performance under an assumption of valid intersection sight distance design. The experimental results show drivers' ability to identify potential hazards to be significantly affected by their IFOVs. As drivers' IFOV improved, drivers were more likely to choose braking actions to avoid collisions. Better IFOVs were also associated with significant increases in brake time to intersection and significant reductions in deceleration rate and crash rate, thus leading to a lower risk of traffic crash involvement. The results indicate that providing a better IFOV for drivers at intersections should be encouraged in practical applications in order to improve drivers' crash avoidance capabilities. Copyright © 2016 John Wiley & Sons, Ltd.     

Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data

Road segments with identical site-specific attributes often exhibit significantly different crash counts due to unobserved reasons. The extent of unobserved heterogeneity associated with a road feature is to be estimated prior to selecting the relevant safety treatment. Moreover, crash count data is often over-dispersed and spatially correlated. This paper proposes a spatial negative binomial specification with random parameters for modeling crash counts of contiguous road segments. The unobserved heterogeneity is incorporated using a finite multi-variate normal mixture prior on the random parameters; this allows for non-normality, skewness in the distribution of the random parameters, facilitates correlation across the random parameters, and relaxes any distributional assumptions. The model extracts the inherent groups of road segments with crash counts that are equally sensitive to the road attributes on an average; the heterogeneity within these groups is also allowed in the proposed framework. The specification simultaneously accounts for potential spatial correlation of the crash counts from neighboring road segments. A Gibbs sampling framework is proposed that leverages recent theoretical developments on data-augmentation algorithms, and elegantly sidesteps many of the computational difficulties usually associated with Bayesian inference of count models. Empirical results suggests the presence of two latent groups and spatial correlation within the study road network. Road features with significantly different effect on crash counts across two latent groups of road segments were identified.     

Distinguishing the land use effects of road pricing based on the urban form attributes

This paper studies the effects of road pricing on land use under different development scenarios (business as usual scenario and transit oriented development scenario) by a quantitative method, which combines the integrated land use and transport interaction model (TRANUS model) with the scenario-planning techniques. Moreover, in order to further analyze the differences of the land use effects of road pricing on traffic analysis zones (TAZs) with different urban form attributes, a quantitative classification method combining factor analysis and cluster analysis is then used to quantitatively classify TAZs. The results demonstrate that the effects of road pricing on the land use of a specific region depend on the urban form attributes of the region. The higher the densities of employments and population, and better street design (high densities of street and intersections) and public transportation condition, the less the region is negatively affected by road pricing, and vice versa. More importantly, rail transit can alleviate the negative impact of road pricing on commercial development and population concentration of the region. Therefore, before introducing a road pricing policy, it is necessary to develop public transport system, especially rail transit.     

Computational methodology to predict injury risk for motor vehicle crash victims: A framework for improving Advanced Automatic Crash Notification systems

Advanced Automatic Crash Notification (AACN) systems, capable of predicting post-crash injury severity and subsequent automatic transfer of injury assessment data to emergency medical services, may significantly improve the timeliness, appropriateness, and efficacy of care provided. The estimation of injury severity based on statistical field data, as incorporated in current AACN systems, lack specificity and accuracy to identify the risk of life-threatening conditions. To enhance the existing AACN framework, the goal of the current study was to develop a computational methodology to predict risk of injury in specific body regions based on specific characteristics of the crash, occupant and vehicle. The computational technique involved multibody models of the vehicle and the occupant to simulate the case-specific occupant dynamics and subsequently predict the injury risk using established physical metrics. To demonstrate the computational-based injury prediction methodology, three frontal crash cases involving adult drivers in passenger cars were extracted from the US National Automotive Sampling System Crashworthiness Data System. The representative vehicle model, anthropometrically scaled model of the occupant and kinematic information related to the crash cases, selected at different severities, were used for the blinded verification of injury risk estimations in five different body regions. When compared to existing statistical algorithms, the current computational methodology is a significant improvement toward post-crash injury prediction specifically tailored to individual attributes of the crash. Variations in the initial posture of the driver, analyzed as a pre-crash variable, were shown to have a significant effect on the injury risk.     

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.