Factors contributing to the severity of intersection crashes

Road crashes are a leading cause of death and serious injuries both developed and developing countries. Intersections are recognized as being among the most hazardous locations on the roads. Although crashes at intersections form about 35 % of the reported accidents account for about 32% of traffic‐related serious injuries and fatalities in Singapore, there is no known study that examines the factors contributing to the severity of these crashes. In this study, the ordinal probit model was applied to crash data from 1992 to 2002 to investigate the role a variety of factors play in determining the severity of intersection crashes. Our study shows that vehicle type, road type, collision type, driver's characteristics and time of day are important determinants of the severity of crashes at intersections in Singapore.     

A method for relating type of crash to traffic flow characteristics on urban freeways

A method is developed to determine how crash characteristics are related to traffic flow conditions at the time of occurrence. Crashes are described in terms of the type and location of the collision, the number of vehicles involved, movements of these vehicles prior to collision, and severity. Traffic flow is characterized by central tendencies and variations of traffic flow and flow/occupancy for three different lanes at the time and place of the crash. The method involves nonlinear canonical correlation applied together with cluster analyses to identify traffic flow regimes with distinctly different crash taxonomies. A case study using data for more than 1000 crashes in Southern California identified twenty-one traffic flow regimes for three different ambient conditions: dry roads during daylight (eight regimes), dry roads at night (six regimes), and wet conditions (seven regimes). Each of these regimes has a unique profile in terms of the type of crashes that are most likely to occur, and a matching of traffic flow parameters and crash characteristics reveals ways in which congestion affects highway safety.     

Crash analysis at intersections in the CBD: A survival analysis model

Enhancing the safety level of urban roads especially in CBDs is paramount. Due to a large number of intersections in what is usually a grid road system in the CBDs, we investigate crashes occurring in and around an intersection. The question of interest in this study is: does the nature of crashes at intersections differ from those of the roads at midblock? Stated more precisely, considering the intersection as a reference point, does the distance to the reference point (i.e. midblock locations on the roads) correlate with different types of crashes compared to that of the intersection? A right answer can lead traffic engineers and safety auditors to propose different safety measures at intersections and the midblock locations. As a pilot study, we collected the last 9 years crash data of the CBD of Melbourne, Australia. For the first time, we employ Survival Analysis models -including Exponential, Weibull, and Log-logistic- to investigate a space-dependent phenomenon (i.e. accidents at proximity to the intersection). Of the outcome, highlights are: (i) police presence at busy intersections during busy night outs and weekends highly improves the pedestrian safety (ii) raised crossings at midblock locations lower likelihood of crashes of pedestrians as well as cars, (iii) lighting conditions at intersections must be watched and kept at a high level. (iv) Severity, likelihood, and location have no known association with the level of congestion. In other words, safety is first, always and everywhere. The results can be of interest to traffic authorities and policy makers in reinforcing traffic calming measures in the cities. The codes developed in this study are made available to the research community to be used in further studies.     

Effectiveness of red light cameras on the right‐angle crash involvement of motorcycles

Red light cameras (RLCs) have been used to reduce right‐angle collisions at signalized intersections. However, the effect of RLCs on motorcycle crashes has not been well investigated. The objective of this study is to evaluate the effectiveness of RLCs on motorcycle safety in Singapore. This is done by comparing their exposure, proneness of at‐fault right‐angle crashes as well as the resulting right‐angle collisions at RLC with those at non‐RLC sites. Estimating the crash vulnerability from not‐at‐fault crash involvements, the study shows that with a RLC, the relative crash vulnerability (RCV) or crash‐involved exposure of motorcycles at right‐angle crashes is reduced. Furthermore, field investigation of motorcycle maneuvers reveal that at non‐RLC arms, motorcyclists usually queue beyond the stop line, facilitating an earlier discharge, and hence become more exposed to the conflicting stream. However at arms with a RLC, motorcyclists are more restrained to avoid activating the RLC and hence become less exposed to conflicting traffic during the initial period of the green. The study also shows that in right‐angle collisions, the proneness of at‐fault crashes of motorcycles is lowest among all vehicle types. Hence motorcycles are more likely to be victims than the responsible parties in right‐angle crashes. RLCs have also been found to be very effective in reducing at‐fault crash involvements of other vehicle types which may implicate exposed motorcycles in the conflicting stream. Taking all these into account, the presence of RLCs should significantly reduce the vulnerability of motorcycles at signalized intersections. Copyright © 2010 John Wiley & Sons, Ltd.     

Congestion and safety: A spatial analysis of London

A disaggregate spatial analysis, using enumeration district data for London was conducted with the aim of examining how congestion may affect traffic safety. It has been hypothesized that while congested traffic conditions may increase the number of vehicle crashes and interactions, their severity is normally lower than crashes under uncongested free flowing conditions. This is primarily due to the slower speeds of vehicles when congestion is present. Our analysis uses negative binomial count models to examine whether factors affecting casualties (fatalities, serious injuries and slight injuries) differed during congested time periods as opposed to uncongested time periods. We also controlled for congestion spatially using a number of proxy variables and estimated pedestrian casualty models since a large proportion of London casualties are pedestrians. Results are not conclusive. Our results suggest that road infrastructure effects may interact with congestion levels such that in London any spatial differences are largely mitigated. Some small differences are seen between the models for congested versus uncongested time periods, but no conclusive trends can be found. Our results lead us to suspect that congestion as a mitigator of crash severity is less likely to occur in urban conditions, but may still be a factor on higher speed roads and motorways.     

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.     

A double standard model for allocating limited emergency medical service vehicle resources ensuring service reliability

This paper introduces a new double standard model (DSM), along with a genetic algorithm (GA), for solving the emergency medical service (EMS) vehicle allocation problem that ensures acceptable service reliability with limited vehicle resources. Without loss of generality, the model is formulated to address emergency services to human injuries caused by vehicle crashes at intersections within an urban street network. The EMS fleet consists of basic life support (BLS) and advanced life support (ALS) vehicles suited for treating crashes with different severity levels within primary and secondary service coverage standards corresponding to extended response times. The model ensures that all demand sites are covered by at least one EMS vehicle within the secondary standard and a portion of which also meets the service reliability requirement. In addition, a portion of demand sites can be covered by at least one of each type of EMS vehicles within the primary standard. Meanwhile, it aims to achieve maximized coverage of demand sites within the primary standard that complies with the required service reliability. A computational experiment is conducted using 2004–2010 data on top two hundred high crash intersections in the city of Chicago as demand sites for model application. With an EMS fleet size of 15 BLS and 60 ALS ambulances maintained by the Chicago Fire Department, at best 92.4–95.5% of demand could be covered within the secondary standard at 90% of service reliability; and 65.5–68.4% of high severity demand and 50.2–54.5 low severity demand could be covered within the primary standard at 90% of service reliability. The model can help optimize EMS vehicle allocation in urban areas.     

Noise level in the vicinity of signalized roundabouts

The analysis, assessment and estimation of noise levels in the vicinity of intersections is a more complex problem than a similar analysis for roads and streets. This is due to the varied geometry of the intersections, differences in the loads of individual movements, participation of heavy vehicles and mass transport vehicles, as well as the various types of traffic management and traffic control. This article analyses the influence of intersection type and traffic characteristics on the noise levels in the vicinity of classic channelized intersections with signalization, roundabouts and signalized roundabouts. Based on the conducted measurements, it has been established that, with comparable traffic parameters and the same distance from the geometric centre of the intersection, the L_Aeq value for signalized roundabouts is 2.5–10.8 dB higher in comparison to classic channelized intersections with signalization and 3.3–6.7 dB higher in relations to the analysed roundabout. Additionally the differences between L_Aeq levels at individual entries at the same signalized roundabouts may reach the value of approximately 4.5 dB. Such situation is influenced by differences in the intersection geometry, diameter of the intersection’s central island, traffic flow type, traffic management at the entries and traffic volume, especially the amount and traffic movements of multiple axle heavy vehicles. These factors have been analysed in detail in relation to signalized roundabouts in this paper.     

Modeling the effect of electric vehicle adoption on pedestrian traffic safety: An agent-based approach

When operated at low speeds, electric and hybrid vehicles have created pedestrian safety concerns in congested areas of various city centers, because these vehicles have relatively silent engines compared to those of internal combustion engine vehicles, resulting in safety issues for pedestrians and cyclists due to the lack of engine noise to warn them of an oncoming electric or hybrid vehicle. However, the driver behavior characteristics have also been considered in many studies, and the high end-prices of electric vehicles indicate that electric vehicle drivers tend to have a higher prosperity index and are more likely to receive a better education, making them more alert while driving and more likely to obey traffic rules. In this paper, the positive and negative factors associated with electric vehicle adoption and the subsequent effects on pedestrian traffic safety are investigated using an agent-based modeling approach, in which a traffic micro-simulation of a real intersection is simulated in 3D using AnyLogic software. First, the interacting agents and dynamic parameters are defined in the agent-based model. Next, a 3D intersection environment is created to integrate the agent-based model into a visual simulation, where the simulation records the number of near-crashes occurring in certain pedestrian crossings throughout the virtual time duration of a year. A sensitivity analysis is also carried out with 9000 subsequent simulations performed in a supercomputer to account for the variation in dynamic parameters (ambient sound level, vehicle sound level, and ambient illumination). According to the analysis, electric vehicles have a 30% higher pedestrian traffic safety risk than internal combustion engine vehicles under high ambient sound levels. At low ambient sound levels, however, electric vehicles have only a 10% higher safety risk for pedestrians. Low levels of ambient illumination also increase the number of pedestrians involved in near-crashes for both electric vehicles and combustion engine vehicles.     

Characterizing on-road variables that affect passenger vehicle modal operation

The current research direction in transportation-related air-quality modeling is towards development and implementation of modal emissions models that correlate emission rates to specific ranges of activity. This paper describes a methodology to identify roadway characteristics at signalized intersections which affect the fraction of vehicle activity spend in specific operating modes where modal emission rate models indicate elevated emissions occur to improve vehicle activity inputs to modal emissions models. Field studies using laser guns were conducted on-road collecting second-by-second activity for individual vehicles at signal-controlled intersections and roadway segments. Hierarchical tree-based regression analysis was used to identify on-road geometric and operational characteristics that influenced the fractions of vehicle activity spent in specific modes. Results indicated that queue position, grade, downstream and upstream per-lane hourly volume, distance to the nearest downstream signalized intersection, percent heavy vehicles, and posted link speed limit were the most statistically significant variables.     

Autonomous vehicle control systems for safe crossroads

This article presents a cooperative manoeuvre among three dual mode cars – vehicles equipped with sensors and actuators, and that can be driven either manually or autonomously. One vehicle is driven autonomously and the other two are driven manually. The main objective is to test two decision algorithms for priority conflict resolution at intersections so that a vehicle autonomously driven can take their own decision about crossing an intersection mingling with manually driven cars without the need for infrastructure modifications. To do this, the system needs the position, speeds, and turning intentions of the rest of the cars involved in the manoeuvre. This information is acquired via communications, but other methods are also viable, such as artificial vision. The idea of the experiments was to adjust the speed of the manually driven vehicles to force a situation where all three vehicles arrive at an intersection at the same time.     

Paradoxes of reservation-based intersection controls in traffic networks

Reservation-based intersection control is a revolutionary idea for using connected autonomous vehicle technologies to improve intersection controls. Vehicles individually request permission to follow precise paths through the intersection at specific times from an intersection manager agent. Previous studies have shown that reservations can reduce delays beyond optimized signals in many demand scenarios. The purpose of this paper is to demonstrate that signals can outperform reservations through theoretical and realistic examples. We present two examples that exploit the reservation protocol to prioritize vehicles on local roads over vehicles on arterials, increasing the total vehicle delay. A third theoretical example demonstrates that reservations can encourage selfish route choice leading to arbitrarily large queues. Next, we present two realistic networks taken from metropolitan planning organization data in which reservations perform worse than signals. We conclude with significantly positive results from comparing reservations and signals on the downtown Austin grid network using dynamic traffic assignment. Overall, these results indicate that network-based analyses are needed to detect adverse route choices before traffic signals can be replaced with reservation controls. In asymmetric intersections (e.g. local road-arterial intersections), reservation controls can cause several potential issues. However, in networks with more symmetric intersections such as a downtown grid, reservations have great potential to improve traffic.     

Real-world gaseous emission characteristics of Euro 6b light-duty gasoline- and diesel-fueled vehicles

To accurately investigate vehicle emissions that have become major contributors to global air pollutants and greenhouse gases, test conditions have been transferred from laboratory type approval test cycles to real-world driving conditions. In this study, the real-world driving emissions of carbon monoxide (CO), total hydrocarbons (THC), nitrogen oxides (NO_x), and carbon dioxide (CO₂) from one gasoline and two diesel Euro 6b light-duty passenger vehicles were investigated by a portable emission measurement system (PEMS) in Lyon, France. NO_x and CO₂ emission controls remain critical to addressing the real-world driving emissions of Euro 6b vehicles. Notably, the tested gasoline vehicle emitted higher CO₂ emissions than diesel vehicles on all types of roads, especially on the urban road with an excess of 29.3–48.3%. The highest emission factors of gaseous pollutants generally occurred on the motorway for the gasoline vehicle, while on the urban road for diesel vehicles. In particular, for high-speed driving conditions, the gasoline vehicle gaseous emissions, especially NO_x emissions, were more affected by acceleration than diesel vehicle emissions. In addition, the CO emissions, especially THC emissions, for the gasoline vehicle, were more influenced by warm-start, especially cold-start, than those for diesel vehicles.     

Abatement cost functions for incentive-based policies in the automobile market

This paper compares the outcomes of policies that target vehicle holdings with those that target vehicle usage using data from the US Consumer Expenditure Survey. Results show that a higher price of gasoline shifts vehicle holdings towards more fuel efficient vehicles and reduces the annual demand for miles, whereas imposing a fee on vehicles or a feebate program only shifts vehicle holdings towards more fuel efficient vehicles and has little to no impact on the demand for miles. While it is relatively expensive to reduce CO₂ emission through incentive-based policies, achieving any abatement level is more expensive through imposing fees on vehicles than gasoline taxes. In addition, the maximum amount of abatement attainable by a feebate program is relatively small and the same amount could be achieved by imposing a $0.73 gasoline tax per gallon.     

A cellular automata (CA) model for motorized vehicle flows influenced by bicycles along the roadside

On many urban low‐grade or branch roads, especially in medium or small cities in China, bicyclists and motorists commonly share the non‐barrier road surface. Because bicycles are unpredictable and unstable when moving, motorized vehicles must reduce their speed to safely approach and overtake them. In this study, the gradual deceleration process a motorized vehicle undergoes before it passes a bicycle was analyzed. The motorist was assumed to prefer a comfortable deceleration and to select a higher deceleration rate only when the distance to the bicycle was insufficient to reduce the car's speed to the expected value at a comfortable deceleration rate. Cellular automata (CA) simulations were used to reveal the flow characteristics of motorized vehicles reacting to bicycles traveling along the roadside, and the results show that for the general velocities of motorized vehicles and bicycles traveling on urban branch roads, the road capacity for motorized vehicles is not related to the number of bicycles present. However, the average travel time of motorized vehicles is significantly affected by the presence of bicycles when the number of motorized vehicles on the road is small. In addition, motorized vehicles' average travel time is more influenced by disturbances in the flow of motorized vehicles than by bicycles when the number of motorized vehicles on the road is large. Field observations and surveys were used to validate the traffic behaviors and simulation results. Copyright © 2016 John Wiley & Sons, Ltd.     

Fatal crash involvement by multiple-trailer trucks

Considerable uncertainty surrounds the relative safety of combination trucks with two or three trailers, compared to those pulling single trailers. This article uses data on conditions present in 5889 fatal crashes involving combination trucks to examine the extent to which these conditions vary for the two configurations. Using two complementary techniques, multiple classification analysis and automatic interaction detector, we evaluate the additive and interactive effects of these conditions. We conclude that multiple-trailer trucks are more likely to be involved in fatal crashes in the following conditions: darkness; snow, slush, or ice on the road surface; involvement of three or more vehicles, indicating at least moderate traffic volume; and higher-speed facilities with 65 to 75 mph limits.     

Signal control optimization for automated vehicles at isolated signalized intersections

Traffic signals at intersections are an integral component of the existing transportation system and can significantly contribute to vehicular delay along urban streets. The current emphasis on the development of automated (i.e., driverless and with the ability to communicate with the infrastructure) vehicles brings at the forefront several questions related to the functionality and optimization of signal control in order to take advantage of automated vehicle capabilities. The objective of this research is to develop a signal control algorithm that allows for vehicle paths and signal control to be jointly optimized based on advanced communication technology between approaching vehicles and signal controller. The algorithm assumes that vehicle trajectories can be fully optimized, i.e., vehicles will follow the optimized paths specified by the signal controller. An optimization algorithm was developed assuming a simple intersection with two single-lane through approaches. A rolling horizon scheme was developed to implement the algorithm and to continually process newly arriving vehicles. The algorithm was coded in MATLAB and results were compared against traditional actuated signal control for a variety of demand scenarios. It was concluded that the proposed signal control optimization algorithm could reduce the ATTD by 16.2–36.9% and increase throughput by 2.7–20.2%, depending on the demand scenario.     

Evolution over time of heavy vehicle volume in toll roads: A dynamic panel data to identify key explanatory variables in Spain

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs. It becomes even more critical for the case of toll roads, which in recent decades has become an increasingly common device to fund road projects. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research on traffic analysis does not often disaggregate heavy vehicle demand from the total volume, so that the specific behavioral patterns of this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990–2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis in 2008.     

Estimation of saturation flow at signalised intersections of developing cities: a micro-simulation modelling approach

Traffic characteristics and operations at the signalised intersections of developing cities are significantly different from those at the similar intersections of cities in developed countries. Considering this, a new microscopic simulation technique, where a co-ordinate approach to modelling vehicle location is adopted, has been used for modelling the traffic operations at signalised intersections of developing cities. The model has been calibrated and validated on the basis of data collected from Dhaka, the capital of Bangladesh. It has been found that the concept of passenger car unit (PCU), which is widely used as a signal design parameter, is not applicable in case of mixed traffic comprising of both motorised and non-motorised vehicles. Therefore, using the developed simulation model the saturation flows at signalised intersections are investigated in an aggregate form of vehicles per hour. It has also been found that saturation flows in terms of aggregate vehicles are very much dependent on the approach width, turning proportion and composition of the traffic mix. Using the simulation results, an equation has also been regressed in order to be able to estimate the saturation flow from the influencing variables like road width, turning proportion, percentage of heavy and non-motorised vehicles.