Infrastructure Engineering - Research Publications
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ItemThe impact of urban road network morphology on pedestrian wayfinding behavior(University of Maine, 2020-01-01)During wayfinding pedestrians do not always choose the shortest available route. Instead, route choices are guided by several well-known wayfinding strategies or heuristics. These heuristics minimize cognitive effort and usually lead to satisfactory route choices. Our previous study evaluated the costs of four well-known pedestrian wayfinding heuristics and their variation across nine network morphologies. It was observed that the variation in the cost of these wayfinding heuristics increased with an increase in the irregularity of the network, indicating that people may opt for more diverse heuristics while walking through relatively regular networks, and may prefer specific heuristics in the relatively irregular ones. The study presented here aims to investigate this claim by comparing simulated routes with observed pedestrian trajectories in Beijing and Melbourne, two cities at opposite ends of the regularity spectrum. We found that the values of mean route length and mean Network Hausdorff Distance for walking trips made in Melbourne were consistently lesser than the corresponding values obtained in Beijing. Also, across both the cities, we found that while there was minimal variation in the popularity of heuristics overall, in cases where different heuristics produced dissimilar routes, the shortest leg first strategy and the least angle strategy were more popular.
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ItemUsing Georeferenced Twitter Data to Estimate Pedestrian Traffic in an Urban Road Network(Dagstuhl Publishing, 2020-09-01)Since existing methods to estimate the pedestrian activity in an urban area are data-intensive, we ask the question whether just georeferenced Twitter data can be a viable proxy for inferring pedestrian activity. Walking is often the mode of the last leg reaching an activity location, from where, presumably, the tweets originate. This study analyses this question in three steps. First, we use correlation analysis to assess whether georeferenced Twitter data can be used as a viable proxy for inferring pedestrian activity. Then we adopt standard regression analysis to estimate pedestrian traffic at existing pedestrian sensor locations using georeferenced tweets alone. Thirdly, exploiting the results above, we estimate the hourly pedestrian traffic counts at every segment of the study area network for every hour of every day of the week. Results show a fair correlation between tweets and pedestrian counts, in contrast to counts of other modes of travelling. Thus, this method contributes a non-data-intensive approach for estimating pedestrian activity. Since Twitter is an omnipresent, publicly available data source, this study transcends the boundaries of geographic transferability and scalability, unlike its more traditional counterparts.