Crossing and Driving Behaviours at Pedestrian Mid-Block Crossing on Urban Arterial

Abstract

Pedestrians are among the most vulnerable road users in Thailand, accounting for approximately 8% of all road traffic fatalities. This study examines the crossing and driving behaviors at pedestrian mid-block crossings on urban arterials by dividing the research into three key parts. The first part evaluates the effectiveness of signalized crosswalks at midblock crosswalks on urban arterial roads. The midblock crosswalk at four sites in three provinces across Thailand was selected as a case study. The study analyzed data before and after the upgrade from a typical zebra crossing to a signalized crosswalk using Measures of Effectiveness (MOEs) in terms of safety including 1) the free flow speeds of motorcycles (MC) and passenger cars (PC), 2) pedestrian behaviors including the change of pedestrians who performed a full crossing inside the crosswalk (called legal crossing), and the waiting time before crossing at the crosswalk area, 3) driver behavior analysis of failure to obey traffic signs and signals and drivers� stopping behaviors, and 4) pedestrians and the driver�s satisfaction with crosswalk improvements. Findings indicate that the free flow speeds of MC and PC decreased by 9-14 km/h (13-18%) and 11-28 km/h (15-32%), respectively. Pedestrian compliance at designated crossings increased by 52-78%, while waiting times before crossing reduced by 11-41 seconds (42-75%). Additionally, driver non-compliance with traffic signs and signals decreased by 10-81%, while stopping behavior improved by 20-93%. Finally, pedestrian and driver satisfaction with crosswalk improvements, reducing pedestrian crash risks, ranged from 70-97% to 78-97%, respectively.Although signalized crosswalks have improved driver-stopping behavior compared to zebra crossings, specific indicators, such as vehicle speed reduction, may not be as significant as expected, drivers frequently violate red-light signals, and pedestrians cross the road violating the pedestrian lights. Therefore, the second part of this study conducts an in-depth investigation into the factors influencing driver-yielding behavior at midblock crosswalks in the four study areas. Field observations were conducted using video recordings to investigate a pedestrian-driver interaction event: a pedestrian entering the crosswalk area while a driver is approaching the crosswalk. A multinomial logistic regression (MLR) model was developed to identify pedestrian and traffic characteristics associated with driver-yielding behavior. The results revealed that the vulnerable pedestrian group, number of pedestrians, position of the pedestrian waiting area at the sidewalk, pedestrian crossing time, number of vehicles, type of vehicles, vehicle speed, headway between the first and second vehicles, post-encroachment time between a pedestrian and vehicle, and roadside parking are the significant factors influencing driver-yielding behavior. The last part of this study investigates factors influencing traffic rule violations among pedestrians, motorcycle riders, and car drivers at midblock crosswalks, which has been improved from a zebra crossing to a raised pedestrian platform. Pedestrians and drivers were randomly interviewed in front of Hat Yai Hospital, Songkhla Province. Data were collected from 500 pedestrians, 300 motorcycle riders, and 350 car drivers through a questionnaire survey based on the extended theory of planned behavior (TPB). Data validation, reliability, and consistency of the factors were performed using Confirmatory Factor Analysis (CFA), and a Structural Equation Model (SEM) was developed to examine the factors influencing the intentions to violate traffic rules. The results demonstrated that attitude was the key predictor of pedestrians� intentions to cross outside crosswalks. On the other hand, subjective norms such as the influence of other riders� traffic violations were the most significantly influenced motorcycle riders, while perceived behavioral control significantly affected car drivers� intentions to not yield to pedestrians. Additionally, perceived risk varied significantly across different crosswalk types, affecting both the likelihood and intention to violate traffic laws. The findings highlight that raised pedestrian platforms and signalized crosswalks significantly improved traffic rules compliance among pedestrians and drivers compared to zebra crossings.The findings from this study contribute to education, engineering, and enforcement strategies and provide policy recommendations for policymakers, engineers, and road safety practitioners to implement safety intervention measures that reduce the likelihood and severity of pedestrian collisions and encourage pedestrians and drivers to comply with traffic laws.??????????????????????????????????????????????????????????????????? ???????????????????????????????????? 8 ????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????? ?????????????????????? 3 ???????? ???????????????????????????????????????????????????????????????????????????????????????? ????????????????????? 4 ?????? 3 ???????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????? (Measures of effectiveness, MOEs) ??????????????? ????????????? 1) ???????????????????????????????????????????? 2) ???????????????????? ?????????? ???????????????????????????????????????????????????? (???????????? ????????????????) ?????????????????????????????? 3) ???????????????????????????????????????????????????????????????????? ???????????????????????????????? ??? 4) ???????????????????????????????????????????????????????? ?????????? ????? ???????????????????????????????????????????????? 9-14 ??./??. (13-18%) ??? 11-28 ??./??. (15-32%) ???????? ?????????????????????????????????????????????????????????? 52-78% ??????????????????????????? 11-41 ?????? (42-75%) ????????? ???????????????????????????????????????????????????? 10-81% ???????????????????????????????? 20-93% ??????? ???????????????????????????????????????????????????????????????????????????????????????????? ?????????? 70-97% ??? 78-97% ???????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????? ???? ???????????????????????? ????????????????????????????? ????????????????????????????????????????????????? ???????????????????????????????????????????????? ??????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????? (Multinomial logistic regression, MLR) ????????????????????????????????????????????????????????????????????????????????????????????????? ?????????? ????? ?????????????????????? ?????????????? ?????????????????????????? ??????????????? ????????????? ?????????????? ??????????????????? ????????????????????????????????????????? ???????????????????????????????????????????????? ?????????????????? ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????? ????????????????????????????????????? ??????????????????????????????????????????????????????? ??????????????????????????????????????? ?????????????????????????? ???????????? ?????????????????????????????? 500 ?? ?????????????????????? 300 ?? ?????????????????? 350 ?? ??????????????????????????????????????????? (Theory of planned behavior, TPB) ???????????????? ??????????????? ???????????????????????????? ? ??????????????????????????????????????????????? (Confirmatory Factor Analysis, CFA) ????????????????????????? (Structural Equation Model, SEM) ??????????????????????????????????????????????????????????????? ?????????? ????? ?????????????????????????????????????????????????????????????????????? ???????????? ??????????????????????? ???? ??????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????? ????????? ???????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????? ??????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????? ????????????????????? ??????????????????????????????????????????? ?????? ????????????????????????????????????? ??????????????????????????????????????????? ???????????????????????????????????????????? ????????????????????????????????????????????????????