Supernetwork Representation Formulation of a Multiclass Simultaneous Transportation Equilibrium Model as a Fixed Demand User Equilibrium Problem

A multiclass simultaneous transportation equilibrium model (MSTEM) explicitly distinguishes between different user classes in terms of socioeconomic attributes, trip purpose, pure and combined transportation modes, as well as departure time, all interacting over a physically unique multimodal network. It enhances the prediction process behaviorally by combining the trip generation and departure time choices to trip distribution, modal split, and trip assignment choices in a unified and flexible framework that has many advantages from both supply and demand sides. However, the development of this concept of multiple classes increases the mathematical complexity of travel forecasting models. In this research, the authors reduce this mathematical complexity by using the supernetwork representation formulation of the diagonalized MSTEM as a fixed demand user equilibrium (FDUE) problem.

Exploratory Modeling and Analysis for Transportation: An Approach and Support Tool - TMIP-EMAT

Traditionally, travel forecasting models have been used to provide single point predictions. That is, a single future scenario is developed and the model is applied to that scenario. This approach, however, ignores the deep uncertainty that exists in future land use, demographic, and transportation systems inputs, not to mention the uncertainty that exists in the model itself. More importantly, transportation policy decisions made on the basis of such model outputs may be misguided and ineffective. This paper demonstrates and motivates the use of travel forecasting models in an exploratory manner that accounts for the inherent uncertainties of the future. Specifically, this paper describes the user workflow for a new planning and modeling tool: the Travel Model Improvement Program Exploratory Modeling and Analysis Tool (TMIP-EMAT) that has been developed to facilitate the use of exploratory techniques with travel forecasting models. Examples from the proof of concept deployment using the Greater Buffalo-Niagara Regional Transportation Council regional travel demand model are included. The goal of the longer term study is to provide TMIP-EMAT for state and regional transportation planning agencies to assess how technological innovations will affect traffic and transit demand on major corridors 20 to 30 years down the road. The tool will illuminate interactions between transportation supply and demand on urban surface transportation systems (especially at the corridor level) through exploratory modeling and simulation, and facilitate insights into potential, possible, plausible, probable or preferred futures.

Air passenger travel forecasting model based on both dynamical individual behavior and social influence force

Air passenger travel forecasting is necessary and becomes very valuable for airline company, because accurately obtaining practical requirements of air passenger, which can not only help airline company to improve air passenger satisfaction degree and enhance user experience so as to gain huge revenue, but also can help air passengers discover suitable travel plan quickly. In order to generate the air passenger travel forecasting model, this paper aims to analyze the internal driving force and social affect factor simultaneously, which was based on dynamical personal behaviors and air passenger social relationship exactly. In particular, three aspects in terms of dynamical personal behaviors, effect of fellow air passenger, and influence of similar air passenger are all considered simultaneously, and then the data from these aspects are further trained so as to obtain weight allocation in many different scenarios. Besides, workday and non-workday are separately considered in order to make the forecasting model feasible and effective.

An Origin-Destination Matrix Estimate for Baghdad City Based on GIS

Baghdad suffers a deficiency in the application of urban transportation planning process, especially in selecting the suitable transport policies to solve transportation problems. One of the important inputs to the transportation planning process is the Origin – Destination Matrix. The O-D matrix is the travel demand between the pair of origin and destination zones and is one of the necessary goals of transportation studies. Estimation of an O-D matrix using the conventional process requires the collection of a huge amount of data. In Baghdad city, there has been no O-D matrix formulated. Accordingly, a prior O-D matrix is estimated in this study. The present research methodology is based on the estimated O-D matrix for Baghdad city in 1987 (prior matrix) and then updated to 2014 using the collected traffic count data as the basis for travel forecasting. The results of this study provide a guide to the local transportation agencies to select the right transport policies, maximize their revenue and better allocate their resources.

Evaluating the Ability of Transit Direct Ridership Models to Forecast Medium-Term Ridership Changes: Evidence from San Francisco

Transit direct ridership models (DRMs) are commonly used both for descriptive analysis and for forecasting, but are rarely evaluated for their ability to predict beyond the estimation data set. This research does so, using two DRMs estimated for rail and bus ridership in San Francisco. The models are estimated from 2009 data, applied to predict 2016 conditions, and compared to actual 2016 ridership. Over this period in San Francisco, observed rail ridership increased by 9% whereas observed bus ridership decreased by 13%. The results show that the models predict 2016 ridership about as well as that for 2009. The rail model correctly predicts the direction of change, but underestimates the magnitude of change. The bus model predicts the direction of change incorrectly, with a predicted 2% increase. A series of sensitivity tests are conducted to better understand the factors driving the ridership changes. These tests produce reasonable rail sensitivities, but reveal that the bus model is too sensitive to frequency, potentially because of the difficulty of estimating the coefficient from cross-sectional data when high-frequency transit also occurs in high-density locations. As the travel forecasting community increases its focus on empirically evaluating forecasts beyond a base year, DRMs must be a part of that.

Closing the Induced Vehicle Travel Gap Between Research and Practice

Several studies have rigorously documented the induced travel effect, in which added highway capacity leads to added vehicle travel. Despite the evidence, transportation planning practice does not fully account for this phenomenon, with the result that estimates of the potential congestion-reducing benefits of added highway capacity may be overstated and estimates of potential environmental impacts understated. In 2015, the California Department of Transportation (Caltrans) sponsored a review of applicable induced vehicle travel research that could inform transportation analysis guidance in response to new laws in California such as Senate Bill 743 (S.B. 743), which prohibits the use of vehicle level of service (LOS) and similar measures as the sole basis for determining significant transportation impacts under the California Environmental Quality Act. Instead, vehicle miles traveled was selected to replace LOS under S.B. 743, and along with the new metric there will be a requirement to account for induced travel effects in analysis of roadway capacity expansion projects. The Caltrans review revealed an inconsistent lexicon in academic research and among practitioners, questions about research applicability, limitations in the sensitivity of travel forecasting models, and confusion about the appropriate use of induced vehicle travel elasticities from research. This paper summarizes the Caltrans review and shares the findings to advance understanding of induced vehicle travel effects and suggest steps for additional research.