Estimating short-term travel demand models that incorporate personally owned autonomous vehicles

The adoption of connected and autonomous vehicles (CAVs) is in its infancy. Therefore, very little is known about their potential impacts on traffic. Meanwhile, researchers and market analysts predict a wide range of possibilities about their potential benefits and the timing of their deployments. Planners traditionally use various types of travel demand models to forecast future traffic conditions. However, such models do not yet integrate any expected impacts from CAV deployments. Consequently, many long-range transportation plans do not yet account for their eventual deployment. To address some of these uncertainties, this work modified an existing model for Madison, Wisconsin. To compare outcomes, the authors used identical parameter changes and simulation scenarios for a model of Gainesville, Florida. Both models show that with increasing levels of CAV deployment, both the vehicle miles traveled and the average congestion speed will increase. However, there are some important exceptions due to differences in the road network layout, geospatial features, sociodemographic factors, land-use, and access to transit.

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PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

Jurnal Sains dan Teknologi Indonesia ◽

10.29122/jsti.v12i3.861 ◽

2013 ◽

Vol 12 (3) ◽

Author(s):

Rusmadi Suyuti

Keyword(s):

Information System ◽

Real Time ◽

Travel Demand ◽

Urban Traffic ◽

Road User ◽

Traffic Information ◽

Estimation Methods ◽

Traffic Counts ◽

Real Time Traffic ◽

Demand Models

Traffic information condition is a very useful information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.

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Address-based computation of intra-cell distances for travel demand models

Procedia Computer Science ◽

10.1016/j.procs.2021.03.023 ◽

2021 ◽

Vol 184 ◽

pp. 123-130

Author(s):

Matthias Heinrichs ◽

Rita Cyganski ◽

Daniel Krajzewicz

Keyword(s):

Travel Demand ◽

Demand Models

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A conceptual framework for modeling the supply side of mobility services within large-scale agent-based travel demand models

Transportation Letters ◽

10.1080/19427867.2021.1913303 ◽

2021 ◽

pp. 1-10

Author(s):

Francisco Calderón ◽

Eric J. Miller

Keyword(s):

Conceptual Framework ◽

Large Scale ◽

Travel Demand ◽

Supply Side ◽

Agent Based ◽

Demand Models

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A copula-based continuous cross-nested logit model for tour scheduling in activity-based travel demand models

Transportation Research Part B Methodological ◽

10.1016/j.trb.2021.01.001 ◽

2021 ◽

Vol 145 ◽

pp. 324-341

Author(s):

Sepehr Ghader ◽

Carlos Carrion ◽

Liang Tang ◽

Arash Asadabadi ◽

Lei Zhang

Keyword(s):

Logit Model ◽

Travel Demand ◽

Nested Logit ◽

Nested Logit Model ◽

Demand Models ◽

Tour Scheduling

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Ensemble learning activity scheduler for activity based travel demand models

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2021.102972 ◽

2021 ◽

Vol 123 ◽

pp. 102972

Author(s):

Mohammad Hesam Hafezi ◽

Naznin Sultana Daisy ◽

Hugh Millward ◽

Lei Liu

Keyword(s):

Ensemble Learning ◽

Travel Demand ◽

Learning Activity ◽

Demand Models

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Regional Bicycle Network Evaluation and Strategic Planning: A Quantitative Methodological Approach Despite Limited Data Sources for Cycling

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211028870 ◽

2021 ◽

pp. 036119812110288

Author(s):

Alex van Dulmen ◽

Martin Fellendorf

Keyword(s):

Travel Demand ◽

Choice Model ◽

Methodological Approach ◽

Data Availability ◽

Data Sources ◽

Test Case ◽

Demand Model ◽

Limited Data ◽

Demand Models ◽

Bicycle Infrastructure

In cases where budgets and space are limited, the realization of new bicycle infrastructure is often hard, as an evaluation of the existing network or the benefits of new investments is rarely possible. Travel demand models can offer a tool to support decision makers, but because of limited data availability for cycling, the validity of the demand estimation and trip assignment are often questionable. This paper presents a quantitative method to evaluate a bicycle network and plan strategic improvements, despite limited data sources for cycling. The proposed method is based on a multimodal aggregate travel demand model. Instead of evaluating the effects of network improvements on the modal split as well as link and flow volumes, this method works the other way around. A desired modal share for cycling is set, and the resulting link and flow volumes are the basis for a hypothetical bicycle network that is able to satisfy this demand. The current bicycle network is compared with the hypothetical network, resulting in preferable actions and a ranking based on the importance and potentials to improve the modal share for cycling. Necessary accompanying measures for other transport modes can also be derived using this method. For example, our test case, a city in Austria with 300,000 inhabitants, showed that a shift of short trips in the inner city toward cycling would, without countermeasures, provide capacity for new longer car trips. The proposed method can be applied to existing travel models that already contain a mode choice model.

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From Macro to Microscopic Trip Generation: Representing Heterogeneous Travel Behavior

The Open Transportation Journal ◽

10.2174/1874447801711010031 ◽

2017 ◽

Vol 11 (1) ◽

pp. 31-43 ◽

Cited By ~ 1

Author(s):

Rolf Moeckel ◽

Leta Huntsinger ◽

Rick Donnelly

Keyword(s):

Travel Behavior ◽

Processing Time ◽

Travel Demand ◽

Computer Processing ◽

Professional Judgment ◽

Trip Generation ◽

Household Type ◽

Demand Models ◽

Generation Mode ◽

Definition Of

Background: In four-step travel demand models, average trip generation rates are traditionally applied to static household type definitions. In reality, however, trip generation is more heterogeneous with some households making no trips and other households making more than a dozen trips, even if they are of the same household type. Objective: This paper aims at improving trip-generation methods without jumping all the way to an activity-based model, which is a very costly form of modeling travel demand both in terms of development and computer processing time. Method: Two fundamental improvements in trip generation are presented in this paper. First, the definition of household types, which traditionally is based on professional judgment rather than science, is revised to optimally reflect trip generation differences between the household types. For this purpose, over 67 million definitions of household types were analyzed econometrically in a Big-Data exercise. Secondly, a microscopic trip generation module was developed that specifies trip generation individually for every household. Results: This new module allows representing the heterogeneity in trip generation found in reality, with the ability to maintain all household attributes for subsequent models. Even though the following steps in a trip-based model used in this research remained unchanged, the model was improved by using microscopic trip generation. Mode-specific constants were reduced by 9%, and the Root Mean Square Error of the assignment validation improved by 7%.

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