New Data and Methods for Modelling Future Urban Travel Demand: A State of the Art Review

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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Urban travel demand: The impact of Box-Cox transformations with nonspherical residual errors

Transportation Research Part B Methodological ◽

10.1016/0191-2615(87)90041-5 ◽

1987 ◽

Vol 21 (6) ◽

pp. 443-477 ◽

Cited By ~ 19

Author(s):

Marcel G. Dagenais ◽

Marc J.I. Gaudry ◽

Tran Cong Liem

Keyword(s):

Travel Demand ◽

Urban Travel ◽

The Impact ◽

Residual Errors

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An application of Monte Carlo simulation: Activity based urban travel demand forecast modeling

2010 International Conference On Computer Design and Applications ◽

10.1109/iccda.2010.5541175 ◽

2010 ◽

Author(s):

Min Chen ◽

Jian Lu ◽

Yunhong Shao

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Travel Demand ◽

Demand Forecast ◽

Urban Travel ◽

Travel Demand Forecast ◽

Forecast Modeling

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

Sustainability ◽

10.3390/su11195525 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5525 ◽

Cited By ~ 6

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.

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SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE

PROMET - Traffic&Transportation ◽

10.7307/ptt.v27i6.1734 ◽

2015 ◽

Vol 27 (6) ◽

pp. 529-538 ◽

Cited By ~ 8

Author(s):

Ying-En Ge ◽

Olegas Prentkovskis ◽

Chunyan Tang ◽

Wafaa Saleh ◽

Michael G. H. Bell ◽

...

Keyword(s):

Travel Behavior ◽

Traffic Congestion ◽

Travel Demand ◽

State Of The Art ◽

Demand Management ◽

Relevant Literature ◽

Short Review ◽

Active Management ◽

Demand Side ◽

Travel Demand Management

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks.

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