Perceived level of service at signalized intersections under heterogeneous traffic conditions

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.

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2634-13 ◽

2017 ◽

Vol 2634 (1) ◽

pp. 86-94 ◽

Cited By ~ 12

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.

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Assessing the relationship between perceived waiting time and level of service at signalized intersection under heterogeneous traffic conditions

Asian Transport Studies ◽

10.1016/j.eastsj.2020.100024 ◽

2020 ◽

Vol 6 ◽

pp. 100024

Author(s):

Darshana Othayoth ◽

K.V. Krishna Rao

Keyword(s):

Waiting Time ◽

Heterogeneous Traffic ◽

Signalized Intersection ◽

Level Of Service ◽

Traffic Conditions ◽

The Relationship

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

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1802-12 ◽

2002 ◽

Vol 1802 (1) ◽

pp. 97-104 ◽

Cited By ~ 21

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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