Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.     

A note on the interpretation of the dual variables in estimating traffic capacity at signal- controlled road junctions

The values of the dual variables in a constrained optimisation problem can be used to estimate the sensitivity of the optimal value of the objective function to changes in the constraints. Allsop (1972) used standard methods of linear programming to derive expressions for the sensitivity of the reserve capacity at a signal-controlled road junction to various changes in the traffic engineering constraints. That analysis used the assumption that the maximum reserve capacity would be achieved when the cycle time used is maximal. While this normally occurs, some junctions have come to light where a reduction in the cycle time increases the capacity. Allsop's analysis is extended here to account for this possibility.     

道路交通安全评价方法研究

随着道路运输的快速发展,我国的交通安全形势也不容乐观。为更好的确保人民出行安全,需在道路建设初期进行安全评价。本文利用德尔菲法进行两轮专家调查,建立了较为科学完善的道路安全评价指标体系;利用层次分析法合理确定各评价指标权重,从系统工程学角度建立了一套可量化、操作性强的道路安全评价机制。     

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 traffic-flows on vehicular-exhaust emissions at traffic junctions

This paper examines the impact of traffic-flow on CO, NO₂ and PM emissions at two distinct traffic junctions and evaluates the use of emission factors. The study includes three scenarios regarding pollutant emissions, which combine a field, experimental and semi-empirically estimated traffic parameters for free, interrupted and congested traffic-flow conditions. It evaluates the emission patterns for heterogeneity in traffic characteristics of both junctions. The results suggest the corrections to be made to emission factors at traffic junctions for better forecast of air quality.     

A macro model for traffic flow on road networks with varying road conditions

In this paper, we develop a macro traffic flow model with consideration of varying road conditions. Our analytical and numerical results illustrate that good road condition can enhance the speed and flow of uniform traffic flow whereas bad road condition will reduce the speed and flow. The numerical results also show that good road condition can smooth shock wave and improve the stability of traffic flow whereas bad road condition will lead to steeper shock wave and reduce the stability of traffic flow. Our results are also qualitatively accordant with empirical results, which implies that the proposed model can qualitatively describe the effects of road conditions on traffic flow. These results can guide traffic engineers to improve the road quality in traffic engineering. Copyright © 2012 John Wiley & Sons, Ltd.     

Simultaneous control of traffic lights and bus departure for priority operation

This article presents a bus priority method for traffic light control based on two modes of operation: immediate and controlled departure. The immediate departure mode is a standard procedure in which the intersection controller grants priority upon request of the bus. Controlled departure acts to avoid a second stop of the bus at the end of the queue formed during red by holding the bus at the bus stop, while still granting priority to the bus lane. Selection of one of the two modes is based on intersection cost that includes bus delay and the impact on the overall traffic near the intersection. The method is applied in a constant cycle scenario where green recall and green extension can only be granted within certain limits. Numerical examples illustrate the application of the approach.     

Driver's braking behavior approaching pedestrian crossings: a parametric duration model of the speed reduction times

The driver's braking behavior while approaching zebra crossings under different safety measures (curb extensions, parking restrictions, and advance yield markings) and without treatment (baseline condition) was examined. The speed reduction time was the variable used to describe the driver's behavior. Forty‐two drivers drove a driving simulator on an urban scenario in which the baseline condition and the safety measures were implemented. The speed reduction time was modeled with a parametric duration model to compare the effects on driver's braking behavior of vehicle dynamic variables and different countermeasures. The parametric accelerated failure time duration model with a Weibull distribution identified that the vehicle dynamic variables and only the countermeasure curb extensions affected, in a statistically significant way, the driver's speed reduction time in response to a pedestrian crossing. This result shows that the driver, because of the improved visibility of the pedestrian allowed by the curb extensions, was able to receive a clear information and better to adapt his approaching speed to yield to the pedestrian, avoiding abrupt maneuvers. This also means a reduction of likelihood of rear‐end collision due to less aggressive braking. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling the effects of road pricing on traffic using ERP traffic data

Singapore’s Electronic Road Pricing (ERP) system involves time-variable charges which are intended to spread the morning traffic peak. The charges are revised every three months and thus induce regular motorists to re-think their travel decisions. ERP traffic data, captured by the system, provides a valuable source of information for studying motorists’ travel behaviour. This paper proposes a new modelling methodology for using these data to forecast short-term impacts of rate adjustment on peak period traffic volumes. Separate models are developed for different categories of vehicles which are segmented according to their demand elasticity with respect to road pricing. A method is proposed for estimating the maximum likelihood value of preferred arrival time (PAT) for each vehicle’s arrivals at a particular ERP gantry under different charging conditions. Iterative procedures are used in both model calibration and application. The proposed approach was tested using traffic datasets recorded in 2003 at a gantry located on Singapore’s Central Expressway (CTE). The model calibration and validation show satisfactory results.     

交通事故预警系统构建

针对交通安全现状及国内外交通预警发展现状的分析,阐明建立交通事故预警系统的必要性。分析了基于人、车、路、环境四要素的道路交通事故的成因,根据交通事故预警系统设计原则和建立预警系统的目的,采用相关理论,选用合适的交通信息采集技术,建立了交通事故预警系统。该系统包括驾驶员预警子系统、车辆防撞预警子系统、车辆状况预警子系统、道路安全预警子系统和交通气象预警子系统。     

Vehicle insurance and the risk of road traffic accidents

Given the upward trend in incidences of road traffic accidents (RTAs) over recent years, in order to mitigate the financial losses arising from such accidents, governments around the world nowadays generally encourage, or even require, drivers to purchase appropriate vehicle insurance. The primary aim of this study is to examine whether RTAs are directly related to the purchase of vehicle insurance, with our examination of data on vehicle damage insurance policyholders revealing that those drivers who purchase more insurance coverage have a higher probability of being involved in accidents, as a result of which, they will tend to submit more claims. This indicates that insurance coverage might contain information which can be used to assess the probability risk levels of RTAs. We also find that drivers with less safety awareness will tend to purchase more coverage, and that those who purchase more coverage will, in turn, tend to have more accidents and submit more claims. Our findings, which provide a number of road traffic policy implications, would appear to justify the use of the bonus–malus system.     

Stochastic simulation of the traffic of an uncontrolled road intersection

The paper describes the logic and results of a stochastic simulation model for uncontrolled intersections with poor visibility where the right of way belongs to the driver on the right. The model deals with crossings with and without turning movements. Several runs have been made with realistic input values. Finally, conclusions are drawn and traffic volume warrants are proposed for establishing two way stop/yield controls on uncontrolled crossings.     

Nonlinear gating control for urban road traffic network using the network fundamental diagram

This work proposes a nonlinear model predictive controller for the urban gating problem. The system model is formalized based on a research on existing models of the network fundamental diagram and the perimeter control systems. For the existing models, modifications are suggested: additional state variables are allocated to describe the queue dynamics at the network gates. Using the extended model, a nonlinear model predictive controller is designed offering a ‘non‐greedy’ policy compared with previous, ‘greedy’ gating control designs. The greedy and non‐greedy nonlinear model predictive control (NMPC) controllers are compared with a greedy linear feedback proportional‐integral‐derivative (PID) controller in different traffic situations. The proposed non‐greedy NMPC controller outperforms the other two approaches in terms of travel distance performance and queue lengths. The performance results justify the consideration of queue lengths in dynamic modeling, and the use of NMPC approach for controller design. Copyright © 2014 John Wiley & Sons, Ltd.     

On dynamics of traffic queues in a road network with route choice based on real time traffic information

Introducing real time traffic information into transportation network makes it necessary to consider development of queues and traffic flows as a dynamic process. This paper initiates a theoretical study of conditions under which this process is stable. A model is presented that describes within-one-day development of queues when drivers affected by real-time traffic information choose their paths en route. The model is reduced to a system of differential equations with delay. Equilibrium points of the system correspond to constant queue lengths. Stability of the system is investigated using characteristic values of the linearised minimal face flow. A traffic network example illustrating the method is provided.     

A new method for evaluation of level of service in pedestrian facilities

Safe and comfortable walking is essential for pedestrian movement in modern urban transportation systems. Since pedestrian traffic cannot be restricted in some specified streets, some measures for pedestrians have to be taken everywhere in urban areas. This research describes a way to evaluate ordinary sidewalks, and two different methods are proposed. One is an evaluation based on pedestrian behaviour and the other is an evaluation based on pedestrian opinion. Using the indices of pedestrian density and sidewalk width, we can estimate the level of service of sidewalk usage. But generally speaking, since it is not often that a sidewalk is insufficient to deal with pedestrian flow, another approach is necessary for its evaluation, that is, pedestrian awareness of sidewalks must be taken into account. The former method is recommended for all sidewalks, especially with comparatively heavy pedestrian traffic, and the latter method is recommended for ones with light pedestrian traffic.     

Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash‐severity models. The data from 1889 pedestrian‐related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross‐intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness‐of‐fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two‐way carriageway, and those that occurred near tram or light‐rail transit stops. Copyright © 2017 John Wiley & Sons, Ltd.     

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.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.