Modelling Pedestrian Level of Service at Mid-Blocks in Urban Areas
Creators: name: Paul, Deborah
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 400512
subject: Transport engineering

description: Walking is a sustainable mode of transport, an activity almost everyone performs for varying travel distances in everyday life. A pedestrian-friendly environment promoting physical activity is important for a healthy city infrastructure. In major cities, such as Melbourne, walking is becoming uncomfortable and challenging because of the increasing population, with a simultaneous increase in public transport modes and a reduction in car-sharing options. The 2018 survey results of Travel and Activity in Victoria show that 55% of the people travelling in the city for work mainly depend on public transport, walking, or cycling. By 2036, it has been estimated that around 1.4 million people will be walking in Melbourne city. This has drawn attention to creating new measures to assess the comfort of pedestrians who use those facilities. Pedestrian level of service is found to provide the measure of satisfaction experienced by pedestrians while using facilities such as walkways, crossings, and transit areas. The Pedestrian level of service has been evaluated for locations such as walkways, crosswalks, and intersections by collecting information such as walkway factors, pedestrian and road traffic, and demographic features. Additionally, some studies in the past also collected data about the purpose of trips for path users to find the walkway level of service. However, it has been found that existing models do not evaluate the impact of trip purpose on the walkway comfort of users. The variables considered for finding the pedestrian level of service based on comfort also vary based on the research methodology and the research location. To address these issues, the current study has considered all possible variables, such as pedestrian crowd, footpath continuity, opposite direction flow of pedestrians, street characteristics, etc., that impact pedestrian comfort while walking on a footpath in central cities. A machine learning approach has been adopted to develop a model that can evaluate the pedestrian level of service. City planners and local government can use this approach to make changes, provide excellent walking conditions in urban areas, and promote walking as the mode of transport where vehicle movement must be minimised.
This study aims to contribute to the field of active transportation by enhancing the factors and methodology used to find the pedestrian level of service. Firstly, the research analyses and determines the factors that affect pedestrian comfort in midblock walkways during a pandemic under walking restrictions such as social distancing. A similar methodology is used to identify the factors that impact the pedestrian level of service based on comfort for various trip purposes such as education, work, and recreation. Secondly, a machine-learning (ML) model called Extreme Gradient Boosting (XGBoost) is developed using the factors influencing walkway performance and comfort during a pandemic. Thirdly, two ML models- Random Forest (RF) and Light Gradient Boosting Machine (Light GBM) are developed, and their pedestrian level of service predictive capability for various trip purposes is compared to find the model that gives higher accuracy that could be used for modelling pedestrian comfort. Light GBM, in this case, gives 5% to 10% more accuracy for the model prediction than the RF model. 
Data collection is conducted in two rounds. The initial phase of data collection focuses on pedestrian feedback obtained during the pandemic, while the subsequent phase captures data collected after the easing of pandemic-related restrictions. This dataset analyses pedestrian behaviour concerning the three most common trip purposes anticipated in Melbourne Central Business District (CBD). The pedestrian sensors in Melbourne CBD have been installed at various places to monitor pedestrian movements and flow density at different times of day over a year since 2009. The flow rate data combined with subjective pedestrian feedback data provide a reliable dataset to develop a model that optimises the walkway performance according to the needs of pedestrians.
Machine learning models often outperform conventional hybrid models in the transportation sector due to their ability to automatically learn complex, nonlinear relationships from data without the need for manually defined rules or membership functions. This adaptability allows ML models to handle large, high-dimensional datasets and improve predictive accuracy as more data becomes available.
This research study shows that during the pandemic, the personal space pedestrians try to maintain is one of the significant variables that decide the PLOS of the walkways. The other significant variables that contribute to assessing the PLOS in the model are pedestrian density, flow rate, and the continuity of footpaths, which are interrupted by factors such as outdoor dining and street vendors. The accuracy of prediction using the XG Boost model is 66%, and the Area under the curve value is 0.82. The post-pandemic models were created for education, work, and recreation trips. The accuracy of prediction using the RF model is from 65 to 70%, and for the Light GBM model, it is from 70 to 80% for various trip purposes. The value of AUROC values ranges from 0.73 to 0.87. This research shows that pedestrians going for educational purposes mainly concentrate on walking speed, safety from vehicles and safe distance from other travellers. Those using the walkways for work prefer to walk on safe and clean footpath surfaces, maintain their walking speed to get on time, and avoid being disturbed by the noise of high-speed vehicles and detours. Recreational trip users mainly feel comfortable about the smaller number of vehicles on the road close to them and the few pedestrians around them and construction sites if it doesn’t bother them. Shapley Additive Explanations (SHAP) have been used to derive the meaning of the working of ML models for different trip purposes. The SHAP summary plot provides insights into the top influencing variables and the direction of the relationship between those variables and their overall comfort or the PLOS. SHAP plots reveal that for a PLOS B or a good-condition walkway, educational trip users consider buffers for safety and landscaping for comfortable walking. On the other hand, recreational trip users prefer buffers for safety and vehicle volume to be less on the road next to them for perceiving better comfort on walkways. Walkway users who travel to work mainly prefer the noise of high-speed traffic to be reduced for safety and the lesser pedestrian crowd around them for comfort.
This research benefits the field of active transportation by providing a feature selection technique that can help to identify the significant variables that influence the pedestrian level of service during specific social climates or underlying conditions that impact pedestrian comfort in the region. The machine learning models that are suggested by this study give insight into the pedestrian level of service modelling using the significant variables, which are qualitative and quantitative, that influence pedestrian comfort while using those walkways. Thus, the predictive models can be used to identify the present condition of city walkways.