Development of an urban travel demand model of the Town of Oakville

This dissertation documents the development of a travel demand model of the Town of Oakville. The purpose of the model was to predict traffic demand on study area roadways during the morning peak hour. The model focused on the prediction of auto driver trips. The model was developed based on the 1996 Transportation Tomorrow Survey data, available from the Data Management Group at the University of Toronto. The model was developed in accordance with the Urban Transportation Modelling System (UTMS) of models. Activities included: Trip Generation, Trip Distribution, and Trip Assignment. Accounting for Mode Split was unnecessary as the model focused only on auto driver trips. The final product of the model is a set of traffic flows over links in the transportation network. The model was developed using TransCAD transportation GIS (Geographic Information System) software. TransCAD is a microcomputer software specifically designed for transportation planning and data management.

Development of an urban travel demand model of the Town of Oakville

This dissertation documents the development of a travel demand model of the Town of Oakville. The purpose of the model was to predict traffic demand on study area roadways during the morning peak hour. The model focused on the prediction of auto driver trips. The model was developed based on the 1996 Transportation Tomorrow Survey data, available from the Data Management Group at the University of Toronto. The model was developed in accordance with the Urban Transportation Modelling System (UTMS) of models. Activities included: Trip Generation, Trip Distribution, and Trip Assignment. Accounting for Mode Split was unnecessary as the model focused only on auto driver trips. The final product of the model is a set of traffic flows over links in the transportation network. The model was developed using TransCAD transportation GIS (Geographic Information System) software. TransCAD is a microcomputer software specifically designed for transportation planning and data management.

Optimal transportation, modelling and numerical simulation

We present an overviewof the basic theory, modern optimal transportation extensions and recent algorithmic advances. Selected modelling and numerical applications illustrate the impact of optimal transportation in numerical analysis.

Estimation of Pre-COVID19 Daily Ridership Patterns From Paper and Electronic Ticket Sales Data With Origin-Destination, Time-Of-Day, and Train-Start Detail on a Commuter Railroad: Quick-Response Big Data Analytics in a World Steeped With Tradition

Our niche method independently estimates hourly commuter rail station-to-station origin-destination (OD) matrix data each day from ticket sales and activation data from four sales channels (paper/mobile tickets, mail order, and onboard sales) by extending well-established transportation modelling methodologies. This algorithm’s features include: (1) handles multi-pack pay-per-ride fare instruments not requiring electronic validation, like ten-trip paper tickets “punched” onboard by railroad conductors; (2) correctly infers directionality for direction-agnostic ticket-types; (3) estimates unlimited ride ticket utilization patterns sufficiently precisely to inform vehicle assignment/scheduling; (4) provides integer outputs without allowing rounding to affect control totals nor introduce artifacts; (5) deals gracefully with cliff-edge changes in demand, like the COVID19 related lockdown; and (6) allocates hourly traffic to each train-start based on passenger choice. Our core idea is that the time of ticket usage is ultimately a function of the time of sale and ticket type, and mutual transformation is made via probability density functions (“patterns”) given sufficient distribution data. We generated pre-COVID daily OD matrices and will eventually extend this work to post-COVID inputs. Results were provided to operations planners using visual and tabular interfaces. These matrices represent data never previously available by any method; prior OD surveys required 100,000 respondents, and even then could neither provide daily nor hourly levels of detail, and could not monitor special event ridership nor specific seasonal travel such as summer Friday afternoons.

Trip Generation Rates Using Household Surveys in the State of Qatar

Investment in transportation can bring a range of economic, social, and environmental benefits. In order to manage resources effectively and to choose the best decision from a variety of investment options for the transportation projects, transportation model is normally used, moreover it can help in predicting the impact of these transportation project options on traveler’s mobility based on future changes in land uses, population, jobs, and other economic factors. Transportation modelling outputs will support in assessing transportation project options and setting the transportation investments priorities. Trip generation is considered the first step in four-stage transport modelling. It estimates the number of trips produced or attracted by households' members over one full day. In the paper, trip generation regression models were developed using household surveys for villas and apartments. The regression models for Villa is (0.357+1.3681X1+2.4914X2) were X1 and X2 and the number of people with driving license and number of active people (employees and students) respectively with an R2 of 0.65 , on the other hand the regression model for the apartment is (0.5323+0.9815X1+2.3961X2) with R2 of 0.54.

Integrated land use and transportation modelling and planning: A South African journey

Confronted by poverty, income disparities and mounting demands for basic services such as clean water, sanitation and health care, urban planners in developing countries like South Africa, face daunting challenges. This paper explores the role of Integrated land use and transportation modelling in metropolitan planning processes aimed at improving the spatial efficiency of urban form and ensuring that public sector investments in social and economic infrastructure contribute to economic growth and the reduction of persistent poverty and inequality. The value of such models is not in accurately predicting the future but in providing participants in the (often adversarial) planning process with a better understanding of cause and effect between different components of the urban system and in discovering common ground that could lead to compromise. This paper describes how an Urban Simulation Model was developed by adapting one of the leading microsimulation models (UrbanSim) originating from the developed world to South African conditions and how the requirements for microscopic data about the base year of a simulation were satisfied in a sparse data environment by introducing various typologies. A sample of results from three case studies in the cities of Tshwane, Ekurhuleni and Nelson Mandela Bay between 2013 and 2017 are then presented to illustrate how modelling supports the planning process by adding elements of rational analysis and hypothesis testing to the evaluation of proposed policies.