Understanding Traveler Behavior Key to Enhancing Walkability in CitiesNovel Model Sheds Light on Walkability Factors for Pedestrians in Cities

The model of global-local path choice is an innovative approach that explains the factors that influence pedestrians' choices of global and local paths in cities. In this instance, Street 1, which is visually appealing but not as efficient, is preferred by pedestrians. This model, developed by Professor Yuki Oyama from Shibaura Institute of Technology, sheds light on the importance of understanding traveler behavior to improve the walkability of cities.

Walking as a sustainable mode of urban mobility is gaining popularity in many cities around the world. Enhancing walkability has numerous benefits, such as promoting good health, reducing traffic, and mitigating air pollution. To achieve this, it is crucial to analyze the factors that contribute to walkability in cities.

One key aspect of this analysis is understanding the behavior of travelers. Before and during their journey, various factors can influence their choice of path. For example, they may plan to take the most efficient route but could encounter obstacles along the way, leading to changes in their route. Factors like the condition of the surface, traffic lights, or the scenic view can also influence their decisions.

As a result, the path choices of travelers can be divided into two categories - global path preferences and local responses to perceived path attributes. While global preferences, such as distance, may play a role, pedestrians often prioritize locally perceived attributes. Therefore, it is necessary to understand the extent to which these attributes influence the global and local preferences of travelers and how they can inform design policies.

To address this issue, Associate Professor Yuki Oyama developed a novel network path choice model called the global-local path choice model. The details of this model were published in a study in Transportation Research Part A: Policy and Practice.

The model utilizes a reward decomposition approach integrated into a link-based (Markovian) path choice model, which considers path choice as a sequence of link choices towards the destination in a Markovian manner. This approach breaks down the Markovian reward function into a global utility, which is a function of attributes that can be perceived globally, and a local utility, which is a function of attributes that can only be perceived locally from the current location. This allows for empirical analysis of the influence of different attributes on the local and global path choices of travelers.

Dr. Oyama applied this model to a real pedestrian network using GPS data collected from pedestrian movements. In this analysis, the green view index (GVI) was used as a locally perceived attribute, representing the amount of greenery around streets extracted from Google Street View images. The aim was to investigate how visual street quality impacts pedestrian decisions.

The results showed that pedestrians respond to GVI values locally rather than having pre-trip global perceptions of the values. Additionally, the study revealed the significance of the location of interventions, such as increasing greenery, and suggested that these interventions should be placed on streets directly connected to the most heavily trafficked streets.

Dr. Oyama believes that this study has various practical applications, such as a decision-making tool for designing and managing transportation networks and providing visual information for route guidance. Moreover, the model can also be used to understand the routing behavior of other agents, such as animals, beyond just travelers in transportation networks.

In conclusion, this study offers a new perspective on analyzing walkability by considering pedestrians' local responses to the environment. It paves the way for more pedestrian-friendly and environmentally sustainable cities.

Alton Shaffer
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