Determination of Pedestrian Level of Service at Signalized Midblock Locations for Mixed Traffic Conditions
Teja Tallam1, K. M. Lakshmana Rao2
1Teja Tallam*, Research Scholar, Jawaharlal Nehru Technological University Hyderabad, India.
2K. M. Lakshmana Rao, Professor, Jawaharlal Nehru Technological University Hyderabad, India. 

Manuscript received on January 09, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on January 30, 2020. | PP: 2751-2755 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6160018520/2020©BEIESP | DOI: 10.35940/ijrte.E6160.018520

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Walking is considered as one of the most important modes of transportation in India. But it is observed that the facilities for the pedestrians are ignored during design, planning and maintenance stage. But these days due to increase in population in urban areas, traffic congestion has become a major problem for safe pedestrian crossing. It is necessary to objectively quantify how well roadways accommodate pedestrian travel. Estimation of pedestrian level of service (PLOS) is the most common approach to assess quality of operations of pedestrian facilities. Due to more urbanisation and also large distance between the successive intersections people are forced to cross at their respective midblock. This paper aims in understanding pedestrian characteristics or pedestrian behaviour which is a fundamental in pedestrian planning process and finding the level of service for the pedestrians (PLOS) at selected signalised midblock. Pedestrian data required was collected using video graphic technique during two peak hours in a day at Kukatpally and Nizampet signalised midblocks in Hyderabad city. The factors considered for the calculation of PLOS are their delay, crossing time, speed, density and volume of pedestrians. Greenshields’s macroscopic model was used to resolve important parameters like free speed (vf) and jam density (kj) by plotting their respective graphs. Finally, regression analysis is carried in R software to calculate pedestrian LOS using the above factors considered. Clustering technique is used to obtain the LOS scores for the collected pedestrian data. LOS calculated from model outputs is compared with the values in Indo HCM 2017.
Keywords: Pedestrian LOS, Signalised Midblock, Greenshield’s Model, Indo HCM 2017, R studio.
Scope of the Article: Network Traffic Characterization and Measurements.