scholarly journals Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data

2021 ◽  
Vol 130 ◽  
pp. 103291
Author(s):  
Sebastian Hörl ◽  
Milos Balac
Keyword(s):  
Travel Demand ◽  
Synthetic Population

scholarly journals Calibration of MATSim in the context of natural hazards in Belgium

2016 ◽  
Author(s):  
Mario Cools ◽  
Ismaïl Saadi ◽  
Ahmed Mustafa ◽  
Jacques Teller
Keyword(s):  
Travel Behavior ◽  
Large Scale ◽  
Travel Demand ◽  
Scale Effects ◽  
Transportation Network ◽  
Transportation Systems ◽  
Generation Process ◽  
Traffic Pattern ◽  
Synthetic Population ◽  
River Floods

In Belgium, river floods are among the most frequent natural disasters and they may cause important changes on travel demand. In this regard, we propose to set up a large scale scenario using MATSim for guarantying an accurate assessment of the river floods impact on the transportation systems. In terms of inputs, agent-based models require a base year population. In this context, a synthetic population with a respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into the dynamic traffic assignment simulator, i.e. MATSim. With respect to data, households travel surveys are the main inputs for synthesizing the populations. Besides, a steady-state inundation map is integrated within MATSim for simulating river floods. To our knowledge, very few studies have focused on how river floods affect transportation systems. In this regard, this research will undoubtedly provide new insights in term of methodology and traffic pattern analysis under disruptions, especially with regard to spatial scale effects. The results indicate that at the municipality level, it is possible to capture the effects of disruptions on travel behavior. In this context, further disaggregation is needed in future studies for identifying to what extent results are sensitive to disaggregation. In addition, results also suggest that the target sub-population exposed to flood risk should be isolated from the rest of the travel demand to reach have more sensitive effects.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4098

Incorporating Land Use into Methods of Synthetic Population Generation and of Transfer of Behavioral Data

2017 ◽  
Vol 2668 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Elizabeth C. McBride ◽  
Adam W. Davis ◽  
Jae Hyun Lee ◽  
Konstadinos G. Goulias
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Travel Demand ◽  
Behavioral Data ◽  
Synthesis Methods ◽  
Population Synthesis ◽  
Household Travel Survey ◽  
Synthetic Populations ◽  
Synthetic Population ◽  
Household Travel

This paper describes a new method of population synthesis that includes land use information. The method is based on an initial identification of suitable land use summaries to build a spatial taxonomy at any spatial scale. This same taxonomy is then used to classify household travel survey records (persons and households) and in parallel geographic subdivisions for the state of California. This land use information is the added dimension in the population synthesis methods for travel demand analysis. Synthetic population generation proceeds by expanding (re-creating) the records of the households responding to the survey and the entire array of travel behavior data reproduced for the synthetic population. The basis for selecting the variables to use in the synthetic population is first testing their significance in simplified specification in models of travel behavior that include land use as an explanatory variable and account for the shape of behavioral data (e.g., observations with no travel). The paper shows differences between synthetic populations with and without land use data to demonstrate the behavioral realism added by this approach.

scholarly journals Population synthesis in activity-based travel demand

2021 ◽  
Author(s):  
Ljupko Šimunović ◽  
Mario Ćosić ◽  
Dino Šojat ◽  
Julijan Jurak
Keyword(s):  
Travel Demand ◽  
Car Ownership ◽  
Population Synthesis ◽  
Demand Model ◽  
Entire Area ◽  
Literature Overview ◽  
Synthetic Population ◽  
Travel Demand Modelling ◽  
Demand Modelling ◽  
Representative Samples

A Synthetic Population is first part of creating travel demand model by using activity-based approach. Population synthesis is application of algorithms that expanded representative samples of people or household with characterises (such as gender, car ownership, age or ethnicity etc.) to entire area of researching. Because of complexity of people decisions before or during travel, one attribute is not enough to fully describe what factors have impact on them. Population synthesis iterate a set of attributes for each person in the sample and after expansion and assigning weights create simulated people or household with their characteristic. Basic components are marginal distribution targets of household and person attributes, household and person samples and algorithm for selecting the sample records into a synthetic population such that the attributes of that population match the marginal targets. Goal of this paper is to present population synthesis and her importance for activity-based approach in travel demand modelling. The paper will consist of introduction, literature overview, presenting benefits and complexity of population synthesis, discussion and conclusion.

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

The Potential GTX Travel Demand using Mobile Phone Data

2019 ◽  
Vol 7 (1) ◽  
pp. 77-84
Author(s):  
Jin Ki Eom ◽  
Kwang-Sub Lee ◽  
Ho-Chan Kwak ◽  
Ji Young Song ◽  
Myeong-Eon Seong
Keyword(s):  
Mobile Phone ◽  
Travel Demand ◽  
Mobile Phone Data

MODEL DISTRIBUSI PERJALANAN PENDUDUK UNTUK PENENTUAN KORIDOR ‘LRT’ DI PULAU BATAM

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Djoko Prijo Utomo
Keyword(s):  
Network Performance ◽  
Travel Demand ◽  
Mass Transit ◽  
Distribution Model ◽  
Economic Activities ◽  
Transit System ◽  
Trip Distribution ◽  
Home Interview ◽  
Interview Survey ◽  
Private Car

In consequence of the increasing of regional economic activities in Pulau Batam, a reliable transportation system is required. Decreasing road network performance as a result of increasing traffic volume needs a strategic planning to anticipate the worsening condition in the future. One of the solutions is by providing mass transit system which is expected to attract private car users. Therefore, determination of potential corridor of mass transit system need to be identified so that the system provide better accessibility. Trip pattern in Pulau Batam must be known by developing trip distribution model. The trip distribution model is calibrated using origin-destination (O-D) data that is based on home interview survey. The validated model will be used to forecast and simulate travel demand onto transport network. Result of model calibration process shows mean trip length difference between model and survey is equal 0.141 %. From simulation of trip assignment is obtained that potential corridor for mass transit system using LRT is Batu Ampar – Batu Aji via Muka Kuning. Passenger forecast in the year 2030 is 193,990 passenger/day (2 directions).

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

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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