Predicting travel flows with spatially explicit aggregate models

Kerkman, Kasper; Martens, Karel; Meurs, Henk

doi:10.1016/j.tra.2018.08.029

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Accordingly, recent studies have started to examine and address the fairness concerns of travel demand forecasting problems spanning across these steps [12], [19], [21], [42], [52], [53]. Specifically, several studies focused on resolving unfairness issues for trip generation forecasting [12], [14], [21], [54]. For example, [12] treated fairness as equal mean per capita travel demand across groups over a period of time and evaluated the fairness issues of several AI methods on demand prediction for ridesourcing services and bike-share systems.…”

Section: B Addressing Ai Fairness Issues In Travel Demand Predictionmentioning

confidence: 99%

Travel Demand Forecasting: A Fair AI Approach

Zhang,

Ke,

Zhao

2024

IEEE Trans. Intell. Transport. Syst.

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Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accurate predictions, may produce prediction biases and raise fairness issues. Using such biased models for decision-making may lead to transportation policies that exacerbate social inequalities. However, limited studies have been focused on addressing the fairness issues of these models. Therefore, in this study, we propose a novel methodology to develop fairness-aware, highly-accurate travel demand forecasting models. Particularly, the proposed methodology can enhance the fairness of AI models for multiple protected attributes (such as race and income) simultaneously. Specifically, we introduce a new fairness regularization term, which is explicitly designed to measure the correlation between prediction accuracy and multiple protected attributes, into the loss function of the travel demand forecasting model. We conduct two case studies to evaluate the performance of the proposed methodology using real-world ridesourcing-trip data in Chicago, IL and Austin, TX, respectively. Results highlight that our proposed methodology can effectively enhance fairness for multiple protected attributes while preserving prediction accuracy. Additionally, we have compared our methodology with three state-of-the-art methods that adopt the regularization term approach, and the results demonstrate that our approach significantly outperforms them in both preserving prediction accuracy and enhancing fairness. This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.

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Section: B Addressing Ai Fairness Issues In Travel Demand Predictionmentioning

confidence: 99%

Travel Demand Forecasting: A Fair AI Approach

Zhang,

Ke,

Zhao

2024

IEEE Trans. Intell. Transport. Syst.

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“…LeSage and Pace [17] proposed the standard spatial autoregressive model (SAM), which considered the interaction among regions. Many researchers applied the model to predict traffic flow [18][19][20][21]. e GWR model is different from SAM by allowing the coefficients of explanatory variables to vary over space [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

Chen

Ochieng

et al. 2021

Journal of Advanced Transportation

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The emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regression (GWR) model to analyze the OD-based ride-sourcing travel flow. It makes a comparison with the existing ordinary least square (OLS) model and spatial autocorrelation model (SAM). We have collected ride-sourcing order data in Hangzhou, China, to provide an accurate source for acquiring ride-sourcing travel flow. We investigate the effects of the residential area, points of interest (POIs), and transit stations on ride-sourcing travel flow among traffic analysis zones (TAZs). The results show the following: (a) GWR has better goodness-of-fit than SAM and OLS. (b) Residential area, enterprise, and bus stations have positive correlations with ride-sourcing OD flows, but education and subway stations have negative correlations. We have further investigated the issue and found that it is not a causal relationship between the bus station and OD flow, due to collinearity between the two variables. The bus station builds on locations with high demand, but its capacity is not large enough to reduce the ride-sourcing flow to a low level, which results in a positive coefficient. (c) Based on the estimated coefficients, the prediction of ride-sourcing flows is feasible, supporting the impact analysis for urban land use and transportation planning. This paper contributes to understanding OD-based ride-sourcing travel flow distributions and provides a framework of long-term OD flow prediction for urban land use and transportation planning.

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Crowd intelligence for sustainable futuristic intelligent transportation system: a review

Shit

2020

IET Intelligent Transport Systems

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Predicting travel flows with spatially explicit aggregate models

Cited by 5 publications

References 32 publications

Travel Demand Forecasting: A Fair AI Approach

Travel Demand Forecasting: A Fair AI Approach

Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

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