Pedestrian Travel Behavior Modeling

2005 ◽  
Vol 5 (2) ◽  
pp. 193-216 ◽  
Author(s):  
Serge P. Hoogendoorn ◽  
Piet H. L. Bovy
Author(s):  
Aman Sharma ◽  
Abdullah Gani ◽  
David Asirvatham ◽  
Riyath Ahmed ◽  
Muzaffar Hamzah ◽  
...  

Author(s):  
Bat-hen Nahmias-Biran ◽  
Yafei Han ◽  
Shlomo Bekhor ◽  
Fang Zhao ◽  
Christopher Zegras ◽  
...  

Smartphone-based travel surveys have attracted much attention recently, for their potential to improve data quality and response rate. One of the first such survey systems, Future Mobility Sensing (FMS), leverages sensors on smartphones, and machine learning techniques to collect detailed personal travel data. The main purpose of this research is to compare data collected by FMS and traditional methods, and study the implications of using FMS data for travel behavior modeling. Since its initial field test in Singapore, FMS has been used in several large-scale household travel surveys, including one in Tel Aviv, Israel. We present comparative analyses that make use of the rich datasets from Singapore and Tel Aviv, focusing on three main aspects: (1) richness in activity behaviors observed, (2) completeness of travel and activity data, and (3) data accuracy. Results show that FMS has clear advantages over traditional travel surveys: it has higher resolution and better accuracy of times, locations, and paths; FMS represents out-of-work and leisure activities well; and reveals large variability in day-to-day activity pattern, which is inadequately captured in a one-day snapshot in typical traditional surveys. FMS also captures travel and activities that tend to be under-reported in traditional surveys such as multiple stops in a tour and work-based sub-tours. These richer and more complete and accurate data can improve future activity-based modeling.


2021 ◽  
Author(s):  
Aya Alkhereibi ◽  
Ali AbuZaid ◽  
Tadesse Wakjira

This paper presents a novel study on the examination of explainable machine learning (ML) technique to predict the mode choice for communities with a majority of blue-collared workers. A total of 4875 trip records for 1050 blue-collared workers have been used to predict their travel mode choices based on 11 trips and socio-economic attributes. The data used in this paper are obtained from the Ministry of Transportation and Communication (MoTC), which targeted blue-collared workers as they represent 89% of the total population in the State of Qatar. A total of four ML models are evaluated to propose the best predictive model. The four models were examined using different performance metrics. The models’ prediction results showed that the random forest (RF) model had the highest accuracy with a predictive accuracy of 0.97. Moreover, SHapley Additive exPlanation (SHAP) approach is used to investigate the significance of the input features and explain the output of the RF model. The results of SHAP analysis revealed that occupation level is the most significant feature that influences the mode choice followed by occupation section, arrival time, and arrival municipality.


Author(s):  
Katherine Shriver

Results of a pedestrian survey conducted in two pairs of Austin, Texas, neighborhoods that possess either pedestrian-oriented or automobile-oriented transportation system, land use, and design characteristics but similar density, housing, and sociodemographic characteristics are reported. Survey results identify the influence of contrasting neighborhood forms on structural characteristics of walk-activity patterns and attitudes as mediated by personal characteristics. Walk-activity patterns significantly vary between neighborhoods with different accessibility characteristics. In the physically accessible neighborhoods, walks are predominantly short and frequent utilitarian trips that involve more secondary activities. Activity in the less accessible neighborhoods is characterized by longer, less frequent recreational walks that involve fewer secondary activities. Results support the conclusion that neighborhood transportation, land use, and design characteristics influence walk distance, duration, purpose, and number of secondary activities. The importance of walking in general and for specific purposes also varies with the relative levels of environmental variables. In the traditional neighborhoods, walkable distances, access to transit, shops, and work are more important, as is the opportunity to be outdoors. In the modern neighborhoods, walkway continuity, trees, and interesting things to look at are more important environmental attributes, as is the opportunity to maintain health. Accessibility affects walk activities as well as associated attitudes, but it is mediated through personal factors such as number of household cars, number of children, and household size. Evidence that pedestrians more highly value the available opportunities for walking suggests that latent predilections towards walking shape residential preferences.


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