Urban Road Links Research Articles

Generalized Linear Modeling of Crashes on Urban Road Links

As crash data have distinctive behavior like over-dispersion, researchers have used statistical methods to deal with this unique behavior of crash data specifically. This study employed generalized linear modeling techniques to develop the model. It was assumed that the accident counts followed negative-binomial distribution, and the link function was chosen to be the log link function. Negative-binomial modeling technique was chosen over Poisson distribution because it is the most used technique by many researchers as crash data may encounter over-dispersion. The accident data set showed greater variability between its variance and mean. The accident frequency distribution is shown in this study that it is highly skewed, with a very high number of road segments registering zero accidents. Negative binomial distribution was chosen over Poisson distribution after comparing Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC). The method is widely applied to count data. Twenty-two parameters were estimated in the model. Since p < 0.05 in the omnibus test, the null hypothesis is rejected, which indicates that the model is reasonably fit. The strongest variables in the model were witnessed to be the length of the links, number of lanes, average daily traffic, bus lane, number of buses and trolleys, and HGVs.

Prediction of travel time on urban road links with and without point detectors

This paper proposes a short-term rolling horizon framework for the within-day prediction of travel times on links with and without point detectors (referred to as observed and unobserved links respectively) along a selected path covered in the Hong Kong journey time indication system (JTIS). In Hong Kong JTIS, the number of point detectors on major roads is usually limited due to the financial budget and site constraints in the densely populated urban area. However, the prediction of the travel times on urban road corridors particularly on the links without point detectors is also valuable to road users and traffic authorities. This paper proposes a 2-stage framework based on functional principal component analysis and maximum likelihood estimation method to predict the mean and standard deviation of the travel times on the study path and observed links as well as unobserved links once every 2 min for the next 30 min. An urban road network in Hong Kong is selected as a case study. The prediction results are validated using an independent dataset from JTIS, demonstrating the practical applicability of the proposed framework.

Analysis of Side Friction Impacts on Urban Road Links

With development in population and vehicular thickness in metropolitan zones in agricultural nations, metropolitan streets present a negative picture with streets not having the option to take into account the limit with respect to which they are intended for, expansion in vehicular populace and blockage alongside lack of metropolitan street space is showed as street side contacts. Side friction factors are the activities identified with the exercises that happen at the edges of the street and here and there inside carriageway of the street, which meddle with the traffic stream along the voyaged way. Erosion factors are been seen to influence the ordinary traffic unfavorably in the heterogeneous rush hour gridlock condition in agricultural nation like India much than the streets of homogeneous traffic. By and large, side gratings go under transports halted in carriageway of transport stops, people on foot strolling in the sides of carriage ways and unlawful stopping and road distributing exercises. The principle point of this research work is to recognize the issues that influence the metropolitan traffic zones because of side friction factors. Keywords: Side friction, traffic congestion, friction factors, carriageway

PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

Quality of Bicycle Traffic Management at Urban Road Links and Signalized Intersections Operating under Mixed Traffic Conditions

This study proposes bicycle level of service (BLOS) models for the urban road links and signalized intersection approaches operating under heterogeneous traffic flow conditions prevailing in developing countries. Extensive data collection was carried out through field investigations, videography techniques, and perception surveys to inspect which parameters primarily affect the quality of bicycling in the mentioned facilities. As many as 74 road links and 70 intersection approaches of nine different Indian cities were included in the study locations. In addition, the perceived satisfaction ratings of 22,320 on-site bicyclists (i.e., 155 bicyclists from each site) were included in the database. These ratings ranged from 1 to 6 (excellent–worst bicycling conditions), and were used as the array of dependent variable in the model development process. Separate BLOS models were developed for road links and bicycle through movement at signalized intersections through the application of a novel evolutionary artificial intelligence technique, namely multi-gene genetic programming. The link model included eight significant variables in total whereas the intersection model included a total of seven. With compacted structures, both models represented excellent reliabilities in the present context and produced high coefficient of determination ( R2) values of above 0.86 with averaged observations. These models will help traffic planners and engineers to evaluate the service levels of urban bicycle facilities of developing countries. Statistical analysis of modeled parameters has reported that the traffic volume and crossing pedestrians have the highest influences on the quality of bicycle traffic management at urban road links and intersection approaches, respectively.

Use of Multisensor Data in Reliable Short-Term Travel Time Forecasting for Urban Roads

As multiple traffic data sources have become available recently, a new opportunity has been provided for improving the accuracy of short-term travel time forecasting by fusing different but valid data sources. However, previous studies seldom quantified and integrated the reliability of data sources into model development to achieve the potential promised by data fusion. This paper proposes a combined method for short-term travel time forecasting for urban road links that uses travel time extracted from fixed vehicle detectors and probe vehicle data. The method uses the generalized autoregressive conditional heteroscedasticity model to forecast the mean and variance of each type of travel time data source, and the Dempster–Shafer model is used to calculate the fusion weights iteratively. Real-world data collected on urban roads in Kunshan, China, were used to validate and evaluate the proposed method. Empirical results show that the proposed method can effectively capture the variance of each type of travel time data source for iteratively calculating the fusion weights and hence can produce accurate travel time forecasts. Moreover, through a comparison with the alternative methods, the proposed method is shown to be able to consistently generate improved performance under varying traffic conditions.

Calibration and transferability of travel time cost functions for urban and suburban roads: a case study in Italy

Cost functions are commonly used in traffic assignments to account for the effects of congestion on road link performances. Despite their influence on assignment results, not many functions are available for estimating travel times on urban or suburban roads, in particular, on roads belonging to historical cities or on roads surrounded by urbanized areas. The solution usually adopted either does not consider the effect of congestion on the travel time, or is based on the Bureau of Public Roads’ (BPR) function. Nonetheless, traffic flow disturbances like side-parking can appreciably affect link travel time resulting in a rise in congestion, especially in the roads of ancient centres. The aim of this paper is threefold: first, to calibrate and validate a travel time function for urban road links; secondly, to investigate the transferability of the cost function to a set of suburban roads; finally, to re-calibrate the cost function in the same suburban context to investigate possible improvements. The cost function included the effect of side-parking, secondary streets and road winding on the worsening of performance due to congestion. Cross-validation results showed the robustness, the transferability and the applicability of the calibrated cost function. Keyword: cost function, travel time, calibration.

Accident prediction models for urban roads

Traffic accidents prediction has an important meaning to the improvement of traffic safety management, and urban traffic accidents prediction model. Different approaches for developing Accident Prediction Models (APMs) are used such as multiple linear regression, multiple logistic regression, Poisson models, negative binomial models, random effects models and various soft computing techniques such as fuzzy logic, artificial neural networks and more recently the neuro-fuzzy systems. This paper reviews application of these approaches for developing APMs and advantages of neuro-fuzzy system in modelling accidents in urban road links and intersections.

Safety Performance Functions for Bicycle Crashes in New Zealand and Australia

After decades of decline, recreational and commuter cycling are becoming more popular in many Australasian cities. This renewed popularity is encouraging from the perspectives of sustainable transport and public health. A major concern to governments at all levels, however, is the higher crash risk that cyclists face, compared with drivers or passengers in motor vehicles, particularly when cyclists ride on roads. Transportation professionals should understand the level of risk that cyclists face within various parts of the road network and the measures that can be employed to mitigate that risk. This paper presents research findings from three main safety studies undertaken in New Zealand with the use of data from cities in New Zealand and in Adelaide, Australia. The research involved both generalized linear modeling and before–after, control–impact methods. Across the various studies, crash, traffic, and cycle volumes and layout data were collected for urban road links, traffic signals, and roundabouts. Flow-only models demonstrated a safety-in-numbers effect: crash risk per cyclist was shown to be lower as cycle volumes increased. When other variables were added to the models, it was possible to understand the impact on various crash types of factors such as road section length, motor vehicle speed, visibility, presence and type of cycle facilities, and lane and road width. Before-and-after analysis was employed to help identify the presence of any bias in the sites that had received cycle facilities. Research findings were mixed on the capacity of cycle facilities to improve safety. Well-designed facilities of adequate width and painted with color appeared to perform best.

Risk factors in urban road traffic accidents

Introduction: The urban road traffic accident (RTA) risks for the city of Zagreb, Croatia, from 1999 through 2000 were analyzed with the aim of reducing the increasing injury incidence. Method: Simple and bivariate analysis using χ 2, odds ratio, and confidence interval of 95% was used to determine risks in three outcome groups: killed, severely, and mildly injured. Results: There were 528 RTA victims consisting of 260 severely, 213 mildly injured, and 55 killed at the scene of an accident and during transportation. More fatal accidents occurred during night hours (OR = 3.78; 95% CI, 2.08–6.85), on urban road links (OR = 2.33; 95% CI, 1.30–4.19), and at exceeding speed limit (OR = 2.56; 95% CI, 1.43–4.61). More people were injured than killed on urban junctions (OR = 5.27; 95% CI, 2.21–12.57). The highest combined risk of dying or being severely injured was found in males, driving at excessive speed, on urban links, and during bad visibility (OR = 16.15; 95% CI, 3.901–66.881). Conclusion: These results will influence the urban traffic police enforcement measures, which will change inappropriate behavior of drivers and protect the least experienced road users.

Influence of Bus Stops on Flow Characteristics of Mixed Traffic

The number and type of bus stops provided on a road significantly influence the flow characteristics of traffic on the road. The study of the influence of bus stops on traffic flow under homogeneous traffic conditions has been attempted by several researchers. However, studies on the influence of bus stops on traffic flow under heterogeneous traffic conditions, are found to be absent. This paper is concerned with the study of heterogeneous traffic flow characteristics using a simulation technique with specific reference to the influence of bus stops on the traffic flow. First, an overview of the microscopic traffic-flow simulation model used to study the flow characteristics of nonlane-based heterogeneous traffic on urban road links is given. Then, the validation of the model based on field observed data is described. Finally, the application of the model to study the impact of curbside bus stops and bus bays on the speeds of other vehicles in heterogeneous traffic streams is dealt with. The effects of variation of the basic parameters, such as bus dwell time, road width, and traffic flow level, are examined for both curbside and bus bay type of stops. The usefulness of the model in fixing the traffic flow levels for replacing curbside stops with bus bays, as example, has also been highlighted.

Accident prediction models for urban roads

This paper describes some of the main findings from two separate studies on accident prediction models for urban junctions and urban road links described in [Uheldsmodel for bygader-Del1: Modeller for 3-og 4-benede kryds. Notat 22, The Danish Road Directorate, 1995; Uheldsmodel for bygader- Del2: Modeller for strækninger. Notat 59, The Danish Road Directorate, 1998] ( Greibe and Hemdorff, 1995, 1998). The main objective for the studies was to establish simple, practicable accident models that can predict the expected number of accidents at urban junctions and road links as accurately as possible. The models can be used to identify factors affecting road safety and in relation to ‘black spot’ identification and network safety analysis undertaken by local road authorities. The accident prediction models are based on data from 1036 junctions and 142 km road links in urban areas. Generalised linear modelling techniques were used to relate accident frequencies to explanatory variables. The estimated accident prediction models for road links were capable of describing more than 60% of the systematic variation (‘percentage-explained’ value) while the models for junctions had lower values. This indicates that modelling accidents for road links is less complicated than for junctions, probably due to a more uniform accident pattern and a simpler traffic flow exposure or due to lack of adequate explanatory variables for junctions. Explanatory variables describing road design and road geometry proved to be significant for road link models but less important in junction models. The most powerful variable for all models was motor vehicle traffic flow.