scholarly journals Estimation of Travel Demand Models with Limited Information: Floating Car Data for Parameters’ Calibration

2021 ◽  
Vol 13 (16) ◽  
pp. 8838
Author(s):  
Antonello Ignazio Croce ◽  
Giuseppe Musolino ◽  
Corrado Rindone ◽  
Antonino Vitetta
Keyword(s):  
Big Data ◽  
Transport System ◽  
Systems Engineering ◽  
Travel Demand ◽  
Transport Systems ◽  
Limited Information ◽  
Sources Of Information ◽  
Floating Car Data ◽  
Demand Models ◽  
System Configurations

This paper attempts to integrate data from models, traditional surveys and big data in a situation of limited information. The goal is to increase the capacity of transport planners to analyze, forecast, and plan passenger mobility. (Big) data are a precious source of information and substantial effort is necessary to filter, integrate, and convert big data into travel demand estimates. Moreover, data analytics approaches without demand models are limited because they allow: (a) the analysis of historical and/or real-time transport system configurations, and (b) the forecasting of transport system configurations in ordinary conditions. Without the support of travel demand models, the mere use of (big) data does not allow the forecasting of mobility patterns. The paper attempts to support traditional methods of transport systems engineering with new data sources from ICTs. By combining traditional data and floating car data (FCD), the proposed framework allows the estimation of travel demand models (e.g., trip generation and destination). The proposed method can be applied in a specific case of an area where FCD are available, and other sources of information are not available. The results of an application of the proposed framework in a sub-regional area (Calabria, southern Italy) are presented.

scholarly journals Big Data Architecture for Intelligent Transport Systems

2019 ◽  
Vol 8 (9) ◽  
pp. 1281-1286
Keyword(s):  
Big Data ◽  
Transport System ◽  
Traffic Management ◽  
Structured Data ◽  
Transport Systems ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Related Data ◽  
Data Architecture ◽  
Smart Traffic

Day by day as the volume of data is being generated massively, storing of data and processing of data becomes a ever growing challenge in intelligent transport system (ITS). In intelligent transport system there are different areas to concentrate like smart parking systems, dynamic toll charging, smart traffic management etc. This paper is mainly focused on big data architecture for intelligent transport system for dynamic toll charging, traffic management and traffic analysis related data collection from various sources. The data collected from various sources can be in the form of structured data, semi structured data and unstructured data. Because of verity of data collected, this paper gives an idea about which data model is appropriate depending on data collected for transportation system.

scholarly journals Transport System Models and Big Data: Zoning and Graph Building with Traditional Surveys, FCD and GIS

2019 ◽  
Vol 8 (4) ◽  
pp. 187 ◽  
Author(s):  
Croce ◽  
Musolino ◽  
Rindone ◽  
Vitetta
Keyword(s):  
Big Data ◽  
Transport System ◽  
Census Data ◽  
Small Data ◽  
Mobility Patterns ◽  
Urban Context ◽  
System Models ◽  
Floating Car Data ◽  
Information And Communication ◽  
Current Stage

The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs’ building in forecasting capabilities, on one side; and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure.

scholarly journals Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

2020 ◽  
Vol 32 (5) ◽  
pp. 711-725
Author(s):  
Dragana Petrović ◽  
Ivan Ivanović ◽  
Vladimir Đorić ◽  
Jadranka Jović
Keyword(s):  
Research Methods ◽  
Travel Demand ◽  
Weather Conditions ◽  
Travel Behaviour ◽  
Transport Systems ◽  
Small Samples ◽  
Local Conditions ◽  
Modal Split ◽  
Demand Models ◽  
The Impact

This paper presents an overview of the applied research methodologies and developed travel demand models that take weather impact into account. The paper deals with trip generation and modal split as elements of travel demand that best describe changes in the travel behaviour in different weather conditions. The authors herein emphasize the importance of research in local conditions in all climate zones, especially in areas where climate and modal split characteristics are different from those in common research areas. This review is designed as a brief guide on how the impact of weather can be explored in order to encourage conducting research even in the countries where there is no systematic traffic and travel data collection. The stated adaptation technique followed by the panel household travel surveys may be particularly appropriate for those countries. It is concluded that small budgets should not be considered an obstacle, because it is possible to draw reliable conclusions based even on small samples. Moreover, modern research methods enable a cheaper survey process together with the possibility of obtaining higher quality of results. The increasing popularity of research in this field should contribute to the creation of more resilient transport systems all over the world. A special contribution of this paper is the review of research studies carried out in central, western and southern Europe and not mentioned in any review paper before.

scholarly journals Determining service provider and transport system related effects of ridesourcing services by simulation within the travel demand model mobiTopp

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Gabriel Wilkes ◽  
Lars Briem ◽  
Michael Heilig ◽  
Tim Hilgert ◽  
Martin Kagerbauer ◽  
...  
Keyword(s):  
Transport System ◽  
Travel Demand ◽  
Waiting Times ◽  
Demand Model ◽  
Agent Based ◽  
Demand Models ◽  
Decision Making Processes ◽  
The City ◽  
As Effects ◽  
Travel Demand Model

Abstract Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services.

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.  

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

2021 ◽  
Vol 184 ◽  
pp. 123-130
Author(s):  
Matthias Heinrichs ◽  
Rita Cyganski ◽  
Daniel Krajzewicz
Keyword(s):  
Travel Demand ◽  
Demand Models

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

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

Ensemble learning activity scheduler for activity based travel demand models

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

Heave Induced Internal Flow Fluctuations in Vertical Transport Systems for Deep Ocean Mining

2014 ◽  
Author(s):  
Stephan D. A. Hannot ◽  
Jort M. van Wijk
Keyword(s):  
Transport System ◽  
Internal Flow ◽  
Deep Ocean ◽  
Vertical Transport ◽  
Transport Systems ◽  
Ship Motions ◽  
Centrifugal Pumps ◽  
Mining System ◽  
Pump System ◽  
Ocean Mining

Deep ocean mining systems will have to operate often in harsh weather conditions with heavy sea states. A typical mining system consists of a Mining Support Vessel (MSV) with a Vertical Transport System (VTS) attached to it. The transport system is a pump pipeline system using centrifugal pumps. The heave motions of the ship are transferred to the pump system due to the riser-ship coupling. Ship motions thus will have a significant influence on the internal flow in the VTS. In this paper, the influence of heave motions on the internal flow in the VTS for a typical mining system for Seafloor Massive Sulfide (SMS) deposits in Papua New Guinea is analyzed. Data on the wave climate in the PNG region is used to compute the ship motions of a coupled MSV-VTS. The ship motions then are translated into forces acting on the internal flow in order to compute fluctuations in the internal flow. In this way, the workability of the mining system with respect to the system’s production can be assessed. Based on a detailed analysis of the internal flow in relation to ship motions, the relevance of a coupled analysis for the design of VTS is made clear. This paper provides a method for performing such analyses.

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