Modeling Pedestrian Delay and Level of Service at Signalized Intersection Crosswalks under Mixed Traffic Conditions

2013 ◽  
Vol 2394 (1) ◽  
pp. 70-76 ◽  
Author(s):  
R. Nagraj ◽  
P. Vedagiri
Keyword(s):  
Signalized Intersection ◽  
Level Of Service ◽  
Mixed Traffic ◽  
Traffic Conditions

Modeling Pedestrian Level of Service at Signalized Intersection Crosswalks under Mixed Traffic Conditions

2015 ◽  
Vol 2512 (1) ◽  
pp. 46-55 ◽  
Author(s):  
Xiaofei Ye ◽  
Jun Chen ◽  
Guiyan Jiang ◽  
Xingchen Yan
Keyword(s):  
Linear Regression ◽  
Pearson Correlation ◽  
Signalized Intersection ◽  
Level Of Service ◽  
Delay Model ◽  
Mixed Traffic ◽  
Factors Affecting ◽  
Traffic Conditions ◽  
Regression Techniques ◽  
Pedestrian Level Of Service

The objectives of this study were to identify the factors affecting the pedestrian level of service (LOS) at signalized intersection crosswalks under mixed traffic conditions and to develop a suitable method for estimating pedestrian LOS. The important factors influencing pedestrian LOS at crosswalks were summarized: turning traffic, through traffic, number of pedestrians, and pedestrian delay. In the Highway Capacity Manual method, pedestrian delay can be calculated by Webster's delay model, which assumes that pedestrians arrive at a uniform rate and comply with the signal at an intersection. However, that assumption is not suitable for the Chinese scenario. A pedestrian delay model was developed by considering nonuniform arrival rates and noncompliant behavior under mixed traffic conditions. The data collected by video and a questionnaire survey include information on 1,257 participants' real-time sense of comfort and safety when crossing five selected intersections and on the operational characteristics of the intersections. With perceived LOS as a dependent variable, Pearson correlation analysis and linear regression techniques were explored to determine the significant factors affecting LOS. To overcome the limitations of linear regression techniques, cumulative logistic regression was done to develop a model that fits mixed traffic conditions in China—a model that can predict the probability of responses within each LOS on the basis of a combination of explanatory variables. The results showed that the cumulative logistic model fit the survey data better than the linear regression model and produces LOS A for the crosswalks.

scholarly journals Passenger Car Unit Estimation at Signalized Intersection for Non-lane Based Mixed Traffic Using Microscopic Simulation Model

2017 ◽  
Vol 45 (1) ◽  
pp. 12 ◽  
Author(s):  
Gowri Asaithambi ◽  
Hayjy Sekar Mourie ◽  
Ramaswamy Sivanandan
Keyword(s):  
Simulation Model ◽  
Signalized Intersection ◽  
Accurate Estimation ◽  
Mixed Traffic ◽  
Passenger Car ◽  
Traffic Conditions ◽  
Passenger Car Unit ◽  
Road Width ◽  
On The Road ◽  
Chennai City

In India, traffic on roads is mixed in nature with widely varying static and dynamic characteristics of vehicles. At intersections, vehicles do not follow ordered queue and lane discipline. Different vehicle types occupy different spaces on the road, move at different speeds, and start at different accelerations. The problem of measuring volume of such mixed traffic has been addressed by converting different vehicles categories into equivalent passenger cars and expressing the volume in terms of Passenger Car Unit (PCU) per hour. The accurate estimation of PCU values for different roadway and traffic conditions is essential for better operation and management of roadway facilities. Hence, the objective of the present study is to estimate the PCU values at signalized intersection in mixed traffic and to study the influence of traffic volume, traffic composition and road width on PCU values.For this purpose, a mixed traffic simulation model developed specifically for a signalized intersection was used. The model was calibrated and validated with the traffic data collected from a signalized intersection in Chennai city. Simulation runs were carried out for various combinations of vehicular composition, volume levels and road width. It was observed that presence of heavy vehicles and increase in road width affects the PCU values. The obtained PCU values were statistically checked for accuracy and proven to be satisfied. The PCU values obtained in this study can be used as a guideline for the traffic engineers and practitioners in the design and analysis of signalized intersections where mixed traffic conditions exist.

scholarly journals PEDESTRIAN SAFETY EVALUATION OF SIGNALIZED INTERSECTIONS USING SURROGATE SAFETY MEASURES

Transport ◽  
2020 ◽  
Vol 35 (1) ◽  
pp. 48-56
Author(s):  
Sankaran Marisamynathan ◽  
Perumal Vedagiri
Keyword(s):  
Safety Evaluation ◽  
Pedestrian Safety ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Mixed Traffic ◽  
Model Framework ◽  
Safety Measures ◽  
Severity Level ◽  
Traffic Conditions ◽  
Surrogate Safety Measures

The large proportions of pedestrian fatalities led researchers to make the improvements of pedestrian safety at intersections. Thus, this paper proposes a methodology to evaluate crosswalk safety at signalized intersections using Surrogate Safety Measures (SSM) under mixed traffic conditions. The required pedestrian, traffic, and geometric data were extracted based on the videographic survey conducted at signalized intersections in Mumbai (India). Post Encroachment Time (PET) for each pedestrian were segregated into three categories for estimating pedestrian–vehicle interactions and Cumulative Frequency Distribution (CDF) was plotted to calculate the threshold values for each interaction severity level. The Cumulative Logistic Regression (CLR) model was developed to predict the pedestrian mean PET values in the cross-walk at signalized intersections. The proposed model was validated with a new signalized intersection and the results were shown that the proposed PET ranges and model appropriate for Indian mixed traffic conditions. To assess the suitability of model framework, model transferability was carried out with data collected at signalized intersection in Kolkata (India). Finally, this study can be helpful to rank the severity level of pedestrian safety in the crosswalk and improve the existing facilities at signalized intersections.

QUANTITATIVE APPROACH TO DETERMINE PEDESTRIAN DELAY AND LEVEL OF SERVICE AT SIGNALIZED INTERSECTION

2018 ◽  
Vol 6 (1) ◽  
pp. 06-13
Author(s):  
Ammu Gopalakrishnan ◽  
Sewa Ram ◽  
Pradip Kumar Sarkar
Keyword(s):  
Empirical Model ◽  
Cycle Time ◽  
Quantitative Approach ◽  
Heterogeneous Traffic ◽  
Signalized Intersection ◽  
Level Of Service ◽  
Physical Parameters ◽  
Linear Regression Models ◽  
Traffic Conditions ◽  
Motorized Vehicles

Purpose: Level of Service is a widely adopted terminology to determine the efficiency of any transport system. From the literature it was studied that the multiple linear regression models established by many researchers to determine PLoS evolved with addition or removal of one or more physical parameters or with respect to the perception of users from different locations. At an intersection, there is little or no established methodology developed so far to determine a quantitative approach for PLoS similar to Vehicular Level of Service (VLoS). It was also pointed out that under heterogeneous traffic conditions, pedestrians are most vulnerable at intersections and they share the same space with motorized vehicles for crossing movements. Methodology: Thus, this study was built on the hypothesis that pedestrian delay of a signalized intersection is quantitatively dependent on pedestrian volume, vehicular volume and cycle time. Two signalized intersections operating as fully actuated and fixed cycle time were considered for study for period of four hours each, covering two hours of morning peak and off-peak hour traffic data. Main Findings: Using various statistical techniques, an empirical model was developed between the pedestrian delay and independent variables namely cycle time, pedestrian volume and vehicular volume. PLoS range was also determined through k-means clustering technique. Implications: The empirical model developed was validated and the application of this research was also explained. Novelty: The study is a new quantitative approach to determine PLoS and was limited to two intersections. Increase in the data may improve the accuracy of the model.

Modeling Pedestrian Level of Service at Signalized Intersection Under Mixed Traffic Conditions

2017 ◽  
Vol 2634 (1) ◽  
pp. 86-94 ◽  
Author(s):  
S. Marisamynathan ◽  
P. Vedagiri
Keyword(s):  
Linear Regression ◽  
Signalized Intersections ◽  
Level Of Service ◽  
Delay Model ◽  
Mixed Traffic ◽  
Threshold Values ◽  
User Perceptions ◽  
Traffic Conditions ◽  
Fcm Clustering ◽  
Pedestrian Level Of Service

Developing countries such as India need to have the proper pedestrian level of service (PLOS) criteria for various facilities to help in planning, designing, and maintaining pedestrian facilities. Thus, the objective of this study was to develop a suitable method for estimating the PLOS model under mixed traffic conditions and also to define threshold values for PLOS classification at signalized intersections. First, the data were collected with video and a user perceptions survey at eight selected signalized intersections in Mumbai, India. Second, pedestrian crossing behaviors were modeled according to arrival pattern, crossing speed, noncompliance behavior, and pedestrian–vehicular interaction. Third, a pedestrian delay model was proposed by considering crossing behavior variations and subsequent validation with field data. Fourth, significant variables were identified on the basis of the Pearson’s correlation test with user’s perceptions score. Fifth, the conventional linear regression (CLR) technique was explored to determine the PLOS. To overcome the limitations of the CLR technique, fuzzy linear regression (FLR) was done to develop a PLOS model that fits mixed traffic conditions in India. Two models were validated, and their statistical performance results indicate that the FLR model predicts the PLOS score more precisely. Finally, k-means and fuzzy C-means (FCM) clustering techniques were applied to classify the PLOS score, and the results were compared by time complexity value and field values. The performance evaluation results indicate that the k-means method saves time but fails to produce more reliable threshold values, and the FCM method produces more accurate and efficient threshold values for the PLOS score at signalized intersections under mixed traffic conditions.

Real-Time Inductive-Signature-Based Level of Service for Signalized Intersections

2002 ◽  
Vol 1802 (1) ◽  
pp. 97-104 ◽  
Author(s):  
Cheol Oh ◽  
Stephen G. Ritchie
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Traffic Information ◽  
Level Of Service ◽  
Self Organizing Map ◽  
Threshold Values ◽  
Support Tool ◽  
Traffic Conditions

The Highway Capacity Manual (HCM) presents a procedure for estimating signalized intersection control delay, which is used to determine level of service (LOS) and to evaluate intersection performance. The HCM is used extensively by traffic engineers. However, it is intended as an offline decision support tool for planning and design. To meet user requirements of advanced traffic management and information systems, new LOS criteria are required for real-time intersection analysis. The objective of this research was to demonstrate a technique for development of such LOS criteria. The study used a new measure of effectiveness, called reidentification delay (RD), derived from analysis of vehicle inductive signatures and reidentification of vehicles traveling through a major signalized intersection in the city of Irvine, California. Two main issues regarding real-time LOS criteria were tackled. The first was how to determine the threshold values partitioning the LOS categories. To provide reliable real-time traffic information, the threshold values should be decided on so that RDs within the same LOS category would represent similar traffic conditions as much as possible. RDs in different LOS categories should also represent dissimilar traffic conditions. The second issue concerned the aggregation interval to use for RD in deriving LOS categories. An investigation of both fixed and cycle-based aggregation intervals was conducted. Several clustering techniques were then employed to derive LOS categories, including k-means, fuzzy, and self-organizing map approaches. The resulting real-time LOS criteria were then determined. The procedures used in this study are readily transferable to other signalized intersections for the derivation of real-time LOS.

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