Estimation of passenger car unit for heterogeneous traffic stream of urban arterials: case study of Kolkata

2017 ◽  
pp. 1-13 ◽  
Author(s):  
Satyajit Mondal ◽  
Sandip Chakraborty ◽  
Sudip Kumar Roy ◽  
Ankit Gupta

In Most Of Developing Countries, The Traffic Is Heterogeneous In Nature Consisting Of Wide Variety Of Vehicles Having Different Dynamic And Static Characteristics. Passenger Car Unit (PCU) / Passenger Car Equivalent (PCE) Values Show A Vital Job In Changing Over Heterogeneous Traffic Stream Into Comparable Homogeneous Traffic, Which Comprises Of Traveller Vehicles As It Were. PCE Values Are Vital In Rush Hour Gridlock Stream Investigations. This Paper Reviews The Previous Researches Carried Out About The Estimation Of Passenger Car Equivalents With Different Performance Measures At Mid-Block Sections And Summarizes PCE Variation With Percentage Of Trucks And Flow Rates In The Tabular Form.


Author(s):  
Raunak Mishra ◽  
Pallav Kumar ◽  
Shriniwas S. Arkatkar ◽  
Ashoke Kumar Sarkar ◽  
Gaurang J. Joshi

This research was aimed at developing an area occupancy–based method for estimating passenger car unit (PCU) values for vehicle categories under heterogeneous traffic conditions on multilane urban roads for a wide range of traffic flow levels. First, PCU values of vehicle categories were determined according to the Transport and Road Research Laboratory definition and replaced the commonly considered measure of performance speed with area occupancy using simulation. The PCU values obtained were found to be significantly different for different volume-to-capacity ratios; this result shows that the PCU value is dynamic in nature. While the dynamic nature of PCU values is well appreciated, practitioners may prefer a single set of optimized PCU values (unique for each vehicle category). Hence, a new method with a matrix solution was proposed to estimate the optimized or unique set of PCU values with area occupancy as the performance measure. To check the credibility of the proposed method, the estimated PCU values were compared from existing guidelines regulated by the Indian Roads Congress (IRC) and values estimated with the widely accepted dynamic PCU concept of speed–area ratio. Results show that the PCU values suggested by IRC and the dynamic PCU concept using the speed–area ratio underestimate and overestimate the flows, respectively, at different traffic volumes. However, the values obtained with the area-occupancy concept were found to be consistent with the traffic flow in a cars-only traffic situation at different flow conditions. The derived set of optimized PCU values proposed can be useful for traffic engineers, researchers, and practitioners for capacity and level-of-service analysis under heterogeneous traffic conditions.


2015 ◽  
Vol 4 (3) ◽  
pp. 34-42
Author(s):  
T. Sri Lakshmi Sowmya ◽  
◽  
A. Ramesh ◽  
B.N.M. Rao ◽  
M. Kumar ◽  
...  

2021 ◽  
pp. 128085
Author(s):  
Krzysztof Danilecki ◽  
Jacek Eliasz ◽  
Piotr Smurawski ◽  
Wojciech Stanek ◽  
Andrzej Szlęk

2013 ◽  
Vol 14 (4) ◽  
pp. 301-306 ◽  
Author(s):  
Anna Fortoul Obermöller

The Case Study section of the International Journal of Entrepreneurship and Innovation serves two purposes. First, the case studies presented are concerned with problematical issues that are pertinent to students of entrepreneurship. Thus they constitute appropriate teaching and learning vehicles on a variety of postgraduate and undergraduate programmes. Each case study is accompanied by a set of guidelines for the use of tutors. Second, it is envisaged that those engaged in entrepreneurial activities will find the cases both interesting and useful. The case of PSA Peugeot Citroën's electric passenger car is an example of an innovation perceived as a failure because of its disappointing sales volume. Yet, by limiting our assessment of the electric passenger car to a short-term perspective, we may miss out on an essential part of its value. As part of a wider innovation process, the electric passenger car project is a significant step for PSA in its expertise regarding electric vehicles. Key learning outcomes: (a) to understand that innovation is a complex process with fuzzy frontiers, both in time and space; (b) to understand that innovation is a long-term investment with spillovers into other projects; (c) to be aware of the multiple perspectives that may be adopted when examining innovation; and (d) to be aware of the impact of labelling a project a failure.


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