Factors affecting pedestrian crossing behaviours at signalized crosswalks in urban areas in Beijing and Singapore

This paper reports an observation investigation of pedestrian crossing behaviors conducted at signalized crosswalks in urban areas in Singapore and Beijing on typical workdays. Each crosswalk was observed 3 times in different periods, i.e. normal hours, lunch hours, and rush hours. A total of 103,956 pedestrians were observed. The results showed that lane type, lane number, intersection type, and culture had significant effect on illegal pedestrian crossing in both cities; observation period had no significant effect on pedestrian violation in both cities; the violation rate in Singapore was lower than that in Beijing. However, observers reported that illegal crossing of vulnerable pedestrians, e.g. pregnant, the lame, old men and women, was more obvious in Singapore than that in Beijing. Evidence proved the hypothesis that the violations were related to pedestrians’ cognition of the definition of safety.

Factors affecting motorcycle crashes at signalized intersections

Singapore crash statistics show that motorcycles are involved in about 54% of crashes at intersections. Moreover, about 46% of fatal and 67% of injury motorcycle crashes occur at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and three-legged signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag 1 dependent specification in the error term is the most suitable. Analysis of the results shows the number of lanes at the intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median at four-legged intersections and an exclusive right-turn lane and an uncontrolled left-turn lane at three-legged intersections exacerbate this potential hazard. Moreover, motorcycle crashes increase on high-speed roadways because of the vulnerability of the motorcyclists. The presence of red light cameras reduces motorcycle crashes significantly on the intersection roadways for both four-legged and three-legged intersections. With the red-light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

Motorcycle safety at signalized intersections

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. ‘Motorcycle safety at signalized intersections’ provides an in-depth understanding of hazards of motorcycles at signalized intersections and suggests some targeted countermeasures to alleviate the problem. Motorcycle safety has been examined by establishing innovative statistical models with detailed investigation on specific maneuver behavior of motorcycles at intersections. Results indicate that motorcyclists are over exposed at signalized intersections and the excess exposure is one of main contributors to their vulnerability at intersections. The presence of red light cameras at intersections not only lower motorcycle crashes due to reduced red-light running in the conflicting stream but also decrease their exposure and hence crashes due to a more restrained discharge pattern of motorcycles. Targeted countermeasures should include deployment of red-light cameras, improvement in motorcyclist visibility and increased awareness on motorcyclist vulnerability.

Safety performance models for urban intersections in Brazil

This paper presents a modeling effort for developing safety performance models (SPM) for urban intersections for three major Brazilian cities. The proposed methodology for calibrating SPM has been divided into the following steps: defining the safety study objective, choosing predictive variables and sample size, data acquisition, defining model expression and model parameters and model evaluation. Among the predictive variables explored in the calibration phase were exposure variables (AADT), number of lanes, number of approaches and central median status. SPMs were obtained for three cities: Fortaleza, Belo Horizonte and Brasilia. The SPM developed for signalized intersections in Fortaleza and Belo Horizonte had the same structure and the most significant independent variables, which were AADT entering the intersection and number of lanes, and in addition, the coefficient of the best models were in the same range of values. For Brasilia, because of the sample size, the signalized and unsignalized intersections were grouped, and the AADT was split in minor and major approaches, which were the most significant variables. This paper also evaluated SPM transferability to other jurisdiction. The SPM for signalized intersections from Fortaleza and Belo Horizonte have been recalibrated (in terms of the COx) to the city of Porto Alegre. The models were adjusted following the Highway Safety Manual (HSM) calibration procedure and yielded C-x of 0.65 and 2.06 for Fortaleza and Belo Horizonte SPM respectively. This paper showed the experience and future challenges toward the initiatives on development of SPMs in Brazil, that can serve as a guide for other countries that are in the same stage in this subject. (C) 2014 Elsevier Ltd. All rights reserved.

Urban intersection improvements for pedestrian safety. Volume II. Identification of safety and operational problems at intersections. Final report.

Federal Highway Administration, Office of Research and Development, Washington, D.C.

Two-phases control of DSRC Vehicle networks for road intersection coexisting safety applications

In this paper we propose a two phases control method for DSRC vehicle networks at road intersection, where multiple road safety applications may coexist. We consider two safety applications, emergency safety application with high priority and routine safety applications with low priority. The control method is designed to provide high availability and low latency for emergency safety applications while leave as much as possible bandwidth for routine applications. It is expected to be capable of adapting to changing network conditions. In the first phase of the method we use a simulation based offline approach to find out the best configurations for message rate and MAC layer parameters for given numbers of vehicles. In the second phase we use the configurations identified by simulations at roadside access point (AP) for system operation. A utilization function is proposed to balance the QoS performances provided to multiple safety applications. It is demonstrated that the proposed method can largely improve the system performance when compared to fixed control method.

Development of accident modification factors on intersection crash maneuvers

In order to estimate the safety impact of roadway interventions engineers need to collect, analyze, and interpret the results of carefully implemented data collection efforts. The intent of these studies is to develop Accident Modification Factors (AMF's), which are used to predict the safety impact of various road safety features at other locations or in upon future enhancements. Models are typically estimated to estimate AMF's for total crashes, but can and should be estimated for crash outcomes as well. This paper first describes data collected with the intent estimate AMF's for rural intersections in the state of Georgia within the United Sates. Modeling results of crash prediction models for the crash outcomes: angle, head-on, rear-end, sideswipe (same direction and opposite direction) and pedestrian-involved crashes are then presented and discussed. The analysis reveals that factors such as the Annual Average Daily Traffic (AADT), the presence of turning lanes, and the number of driveways have a positive association with each type of crash, while the median width and the presence of lighting are negatively associated with crashes. The model covariates are related to crash outcome in different ways, suggesting that crash outcomes are associated with different pre-crash conditions.

Important omitted spatial variables in safety models: Understanding contributing crash causes at intersections

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causation—rests upon the pursuit of numerous lines of inquiry. The research community has focused on analytical methods development (negative binomial specifications, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might think of different lines of inquiry in terms of ‘low lying fruit’—areas of inquiry that might provide significant improvements in understanding crash causation. It is the contention of this research that omitted variable bias caused by the exclusion of important variables is an important line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant ability to better understand contributing factors to crashes. This study—believed to represent a unique contribution to the safety literature—develops and examines the role of a sizeable set of spatial variables in intersection crash occurrence. In addition to commonly considered traffic and geometric variables, examined spatial factors include local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools. The results indicate that inclusion of these factors results in significant improvement in model explanatory power, and the results also generally agree with expectation. The research illuminates the importance of spatial variables in safety research and also the negative consequences of their omissions.

The significance of endogeneity problems in crash models: An examination of left-turn lanes in intersection crash models

Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system. The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature. This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes ‘appear’ to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.

Incorporating weather into regionwide safety planning prediction models

Predicting safety on roadways is standard practice for road safety professionals and has a corresponding extensive literature. The majority of safety prediction models are estimated using roadway segment and intersection (microscale) data, while more recently efforts have been undertaken to predict safety at the planning level (macroscale). Safety prediction models typically include roadway, operations, and exposure variables—factors known to affect safety in fundamental ways. Environmental variables, in particular variables attempting to capture the effect of rain on road safety, are difficult to obtain and have rarely been considered. In the few cases weather variables have been included, historical averages rather than actual weather conditions during which crashes are observed have been used. Without the inclusion of weather related variables researchers have had difficulty explaining regional differences in the safety performance of various entities (e.g. intersections, road segments, highways, etc.) As part of the NCHRP 8-44 research effort, researchers developed PLANSAFE, or planning level safety prediction models. These models make use of socio-economic, demographic, and roadway variables for predicting planning level safety. Accounting for regional differences - similar to the experience for microscale safety models - has been problematic during the development of planning level safety prediction models. More specifically, without weather related variables there is an insufficient set of variables for explaining safety differences across regions and states. Furthermore, omitted variable bias resulting from excluding these important variables may adversely impact the coefficients of included variables, thus contributing to difficulty in model interpretation and accuracy. This paper summarizes the results of an effort to include weather related variables, particularly various measures of rainfall, into accident frequency prediction and the prediction of the frequency of fatal and/or injury degree of severity crash models. The purpose of the study was to determine whether these variables do in fact improve overall goodness of fit of the models, whether these variables may explain some or all of observed regional differences, and identifying the estimated effects of rainfall on safety. The models are based on Traffic Analysis Zone level datasets from Michigan, and Pima and Maricopa Counties in Arizona. Numerous rain-related variables were found to be statistically significant, selected rain related variables improved the overall goodness of fit, and inclusion of these variables reduced the portion of the model explained by the constant in the base models without weather variables. Rain tends to diminish safety, as expected, in fairly complex ways, depending on rain frequency and intensity.

Differences in the performance of safety performance functions estimated for total crash count and for crash count by crash type

In recent years the development and use of crash prediction models for roadway safety analyses have received substantial attention. These models, also known as safety performance functions (SPFs), relate the expected crash frequency of roadway elements (intersections, road segments, on-ramps) to traffic volumes and other geometric and operational characteristics. A commonly practiced approach for applying intersection SPFs is to assume that crash types occur in fixed proportions (e.g., rear-end crashes make up 20% of crashes, angle crashes 35%, and so forth) and then apply these fixed proportions to crash totals to estimate crash frequencies by type. As demonstrated in this paper, such a practice makes questionable assumptions and results in considerable error in estimating crash proportions. Through the use of rudimentary SPFs based solely on the annual average daily traffic (AADT) of major and minor roads, the homogeneity-in-proportions assumption is shown not to hold across AADT, because crash proportions vary as a function of both major and minor road AADT. For example, with minor road AADT of 400 vehicles per day, the proportion of intersecting-direction crashes decreases from about 50% with 2,000 major road AADT to about 15% with 82,000 AADT. Same-direction crashes increase from about 15% to 55% for the same comparison. The homogeneity-in-proportions assumption should be abandoned, and crash type models should be used to predict crash frequency by crash type. SPFs that use additional geometric variables would only exacerbate the problem quantified here. Comparison of models for different crash types using additional geometric variables remains the subject of future research.

Development of accident prediction models for rural highway intersections

A study was done to develop macrolevel crash prediction models that can be used to understand and identify effective countermeasures for improving signalized highway intersections and multilane stop-controlled highway intersections in rural areas. Poisson and negative binomial regression models were fit to intersection crash data from Georgia, California, and Michigan. To assess the suitability of the models, several goodness-of-fit measures were computed. The statistical models were then used to shed light on the relationships between crash occurrence and traffic and geometric features of the rural signalized intersections. The results revealed that traffic flow variables significantly affected the overall safety performance of the intersections regardless of intersection type and that the geometric features of intersections varied across intersection type and also influenced crash type.

Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag 1 dependent specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways of T intersections increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red-light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

Right-angle crash vulnerability of motorcycles at signalized intersections : mixed logit analysis

Motorcycles are particularly vulnerable in right-angle crashes at signalized intersections. The objective of this study is to explore how variations in roadway characteristics, environmental factors, traffic factors, maneuver types, human factors as well as driver demographics influence the right-angle crash vulnerability of motorcycles at intersections. The problem is modeled using a mixed logit model with a binary choice category formulation to differentiate how an at-fault vehicle collides with a not-at-fault motorcycle in comparison to other collision types. The mixed logit formulation allows randomness in the parameters and hence takes into account the underlying heterogeneities potentially inherent in driver behavior, and other unobserved variables. A likelihood ratio test reveals that the mixed logit model is indeed better than the standard logit model. Night time riding shows a positive association with the vulnerability of motorcyclists. Moreover, motorcyclists are particularly vulnerable on single lane roads, on the curb and median lanes of multi-lane roads, and on one-way and two-way road type relative to divided-highway. Drivers who deliberately run red light as well as those who are careless towards motorcyclists especially when making turns at intersections increase the vulnerability of motorcyclists. Drivers appear more restrained when there is a passenger onboard and this has decreased the crash potential with motorcyclists. The presence of red light cameras also significantly decreases right-angle crash vulnerabilities of motorcyclists. The findings of this study would be helpful in developing more targeted countermeasures for traffic enforcement, driver/rider training and/or education, safety awareness programs to reduce the vulnerability of motorcyclists.

Developing targeted countermeasures to improve motorcycle safety

The fatality and injury rate of motorcyclists per registered vehicle are higher than those of other motor vehicles by 13 and 7 times respectively. The crash involvement rate of motorcyclists as a victim party is 58% at intersections and as an offending party is 67% at expressways. Previous research efforts showed that the motorcycle safety programs are not very effective in improving motorcycle safety. This is perhaps due to inefficient design of safety program as specific causal factors may not be well explored. The objective of this study is to propose more sophisticated countermeasures and awareness programs for improving motorcycle safety after analyzing specific causal factors for motorcycle crashes at intersections and expressways. Methodologically this study applies the binary logistic model to explore the at-fault or not-at-fault crash involvement of motorcyclists at those locations. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Results shows that the night time crash occurrence, presence of red light camera, lane position, rider age, licence class, and multivehicle collision significantly affect the fault of motorcyclists involved in crashes at intersections. On the other hand, the night time crash occurrence, lane position, speed limit, rider age, licence class, engine capacity, riding with pillion passenger, foreign registered motorcycles, and multivehicle collision has been found to be significant at expressways. Legislate to wear reflective clothes and using reflective markings on the motorcycles and helmets are suggested as an effective countermeasure for reducing their vulnerability. The red light cameras at intersections reduce the vulnerability of motorcycles and hence motorcycle flow and motorcycle crashes should be considered during installation of red light cameras. At signalized intersections, motorcyclists may be taught to follow correct movement and queuing rather than weaving through the traffic as it leads them to become victims of other motorists. The riding simulators in the training centers can be useful to demonstrate the proper movement and queuing at junctions. Riding with pillion passenger and excess speed at expressways are found to significantly influence the at at-fault crash involvement of the motorcyclists. Hence the motorcyclists should be advised to concentrate more on riding while riding with pillion passenger and encouraged to avoid excess speed at expressways. Very young and very older group of riders are found to be at-fault than middle aged groups. Hence this group of riders should be targeted for safety improvement. This can be done by arranging safety talks and programs in motorcycling clubs in colleges and universities as well as community riding clubs with high proportion of elderly riders. It is recommended that the driving centers may use the findings of this study to include in licensure program to make motorcyclists more aware of the different factors which expose the motorcyclists to crash risks so that more defensive riding may be needed.