scholarly journals DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction

2018 ◽  
Author(s):  
Dongjie Wang ◽  
Yan Yang ◽  
Shangming Ning
Keyword(s):  
Travel Demand ◽  
Demand Prediction ◽  
Spatio Temporal

scholarly journals Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

2021 ◽  
Vol 13 (12) ◽  
pp. 6596
Author(s):  
Riccardo Ceccato ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi
Keyword(s):  
Travel Demand ◽  
Risk Mitigation ◽  
Great Part ◽  
Mitigation Measures ◽  
Sustainable Mobility ◽  
New Normal ◽  
Logistic Regression Models ◽  
Demand Prediction ◽  
Travel Decisions ◽  
The University

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

scholarly journals Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

2019 ◽  
Vol 11 (19) ◽  
pp. 5525 ◽  
Author(s):  
Jinjun Tang ◽  
Fan Gao ◽  
Fang Liu ◽  
Wenhui Zhang ◽  
Yong Qi
Keyword(s):  
New York ◽  
Travel Demand ◽  
Geographical Area ◽  
Temporal Characteristic ◽  
Temporal Variations ◽  
Road Density ◽  
Transit System ◽  
Factors Affecting ◽  
Urban Travel ◽  
Spatio Temporal

Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

Evaluating the Effects of Transportation—Land-Use Policies on Housing Values and Household Welfare

1980 ◽  
Vol 12 (7) ◽  
pp. 747-764 ◽  
Author(s):  
A Anas
Keyword(s):  
Simulation Model ◽  
Discrete Choice ◽  
Transportation Planning ◽  
Large Scale ◽  
Travel Demand ◽  
Choice Theory ◽  
Property Values ◽  
Computationally Efficient ◽  
Demand Prediction ◽  
The Impact

In a previous article published in this journal (Anas, 1979a), a simulation model developed by the author was used to examine the impact of transit investment on property values in an urban transportation corridor that had a completely centralized employment distribution. The present paper examines the effect of rail-transit investment in the context of various scenarios which deal with urban employment decentralization, housing distribution, transportation pricing, and income composition. From these simulations it appears that under a variety of assumptions regarding urban change the taxation of short-run differential changes in property values caused by transit investment can raise only a small portion of the cost of typical transit investments. The distinctive feature of the simulation model is that it is consistent with the discrete-choice theory of travel demand currently used in transportation planning and travel-demand prediction. But whereas the state of the art in transportation planning ignores the simultaneity of transportation changes and price changes in the housing market, the model developed here is a first attempt to deal with these effects by incorporating discrete-choice theory into a Walrasian market-equilibration procedure. In addition to being a theoretical alternative to the classical bid-rent model, still made use of by urban economists, the new approach is computationally efficient and suitable for large-scale simulation.

Sign in / Sign up

Export Citation Format

Share Document