Predicting Change in Average Vehicle Ridership on the Basis of Employer Trip Reduction Plans

Transportation Research Part C: Emerging Technologies, 1996

article explores the application of neural networks to a behavioral transportation planning problem. The motivation for adding neural networks as a new modeling methodology stems from its apparent relevance to problems requiring large scale, highly dimensional, data analysis, such as travel related behavior. Neural networks provide a tool to analyze the data in which we can model our intuition, and they provide that capability without the complication of having to formalize all the complex causal variables and relationships which other models require. The transportation issue explored, upon which the neural network methodology is tested, is a comparison of travel demand patterns of men and women in Israel. The information base is the Traveling Habits Survey (Central Bureau of Statistics, Israel, 1984, Sraristical Abstract of Israel, No. 35) commissioned by the Israel Ministry of Transport; combined with demographic and socioeconomic data of the 1983 Population and Housing Census. As extensive as such surveys are, the neural networks imply that additional categories of data are necessary to predict how these elements relate to travel behavior. This article concentrates on the extent to which neural networks can combine the relative simplicity of aggregate transportation models, with the theoretical advantages and level of detail of disaggregate transportation models, without the latter's complexity. We describe the various directions we took in analyzing complex travel related data with feed forward, backpropagation trained, neural networks.

Journal of Transportation Management, 2010

One information technology that may be considered by transportation managers, and which is included in the portfolio of technologies that encompass TMS. is artificial neural networks (ANNs). These artificially intelligent computer decision support software provide solutions by finding and recognizing complex patterns in data. ANNs have been used successfully by transportation managers to forecast transportation demand, estimate future transport costs, schedule vehicles and shipments, route vehicles and classify earners for selection. Artificial neural networks excel in transportation decision environments that are dynamic, complex and unstructured. This article introduces ANNs to transport managers by describing ANN technological capabilities, reporting the current status of transportation neural network applications, presenting ANN applications that offer significant potential for future development and offering managerial guidelines for ANN development.

Transportation Research Record: Journal of the Transportation Research Board, 2013

The paper compares univariate and multivariate neural network and autoregressive time series models with application to short-term forecasting of freeway speeds. The developed models are evaluated with respect to temporal data resolution, prediction accuracy, and quality of fit using statistical tests. Results indicate that neural networks provide -by and large -more accurate predictions than classical statistical approaches particularly for finer data resolutions. Evaluation of model fit indicates that, in contrast to vector autoregressive models, Neural Networks may also provide unbiased predictions. Overall, our findings clearly suggest the need to jointly consider statistical and Neural Network models in order to develop more efficient prediction models.

arXiv (Cornell University), 2023

This study compares the performance of a causal and a predictive model in modeling travel mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal inference technique were used to extract the causal relationships in the mode choice decision making process and to estimate the quantitative causal effects between the variables both directly from observational data. The model results reveal that trip distance and vehicle ownership are the direct causes of mode choice in the three neighborhoods. Artificial neural network models were estimated to predict mode choice. Their accuracy was over 70%, and the SHAP values obtained measure the importance of each variable. We find that both the causal and predictive modeling approaches are useful for the purpose they serve. We also note that the study of mode choice behavior through causal modeling is mostly unexplored, yet it could transform our understanding of the mode choice behavior. Further research is needed to realize the full potential of these techniques in modeling mode choice.

Transportation Research Part C: Emerging …, 2010

The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.

1991

The purpose of this paper is to describe the development of a model designed to predict A number of the existing models that have been developed around the United States are based on the application of elasticities to estimate passenger response to route-level service changes. A particularly good example of such a model is the one developed by Parsons, Brinkerhoff, Quade & Douglas, Inc. for the Dallas Area Rapid Transit (DART) Bus System . The model is a spreadsheet model that estimates the impact of certain bus service modifications and fare changes on the system's ridership, operating expenses, passenger revenue, and key performance indicators. It is designed to be applied readily and simply by transit agency staff, who have some familiarity with standard spreadsheet software on a microcomputer. The model consists of a detailed route-by-route data base containing current patronage, vehicle miles and hours, service characteristics, and fare; and a look-up table that permits calculation of route costs and patronage changes. The model is designed to provide estimates of the response to the following types of service and fare changes: 9 Peak, off-peak, or weekend headways 9 Peak, off-peak, or weekend service periods 9 Elimination of a route 9 Elimination of service in a period 9 Fare change

Transportation Research Part E: Logistics and Transportation Review, 2000

Understanding and predicting traveller behaviour remains a complex activity. The set of tools in common use by practitioners and many of the tools used by researchers appear in many ways to exhibit complexity; yet often this richness of detail is in methods of estimation rather than in representation of how individuals actually evaluate alternatives and make decisions on a set of interrelated travel choices. Discrete choice methods championed by the multinomial logit model and its variants such as nested logit, heteroskedastic extreme value, and multinomial probit have added substantial behavioural richness into statistical specification and estimation (Hensher et al forthcoming), seeking to accommodate the role of both observed and unobserved influences on travel choices. The search for behavioural and analytical enhancement continues. Research in the field of artificial intelligence systems has been exploring the use of neural networks (eg Faghri and Hua 1991, Yang et al 1993) as a framework within which many traffic and transport problems can be studied. The main motivation for using neural networks could be due to some fascinating properties that neural networks possess. They are parallelism, the capacity to learn, allowing for the use of distributed memory and capacity for generalisation. Following these characteristics, one of the promises from neural networks is that they can tackle the problem of forecasting and modelling which is very common in travel demand modelling. The use of such tools in studying individual traveller behaviour opens up an opportunity to consider the extent to which there are representation frameworks which complement and/or replace existing analytical approaches. This paper explores the merits of neural networks as part of a revised framework within which to explore the processes of traveller decision making, and how discrete choice methods might be integrated within such a framework to acknowledge the important role that the latter tools have played in the last 25 years in the development of better practice in travel demand modelling.

Journal of Civil Engineering, Science and Technology, 2018

Evidence from literature has shown the absence of the use of Artificial Neural Network techniques in formulating trip generation forecasts in Nigeria, rather the practice has consisted more on use of regression techniques. Therefore, in this study, the accuracy of Radial Basis Function Neural Network (RBFNN) and Multiple Linear Regression model (MLR) in formulating home-based trips generation forecasts was assessed. Datasets for the study were acquired from a household travel survey in the high density zones of Akure, Nigeria and were analysed using SPSS 22 statistical software. Results of data analysis showed that the RBFNN model with higher Coefficient of Determination (R2) value of 0.913 and lower Mean Absolute Percentage Error (MAPE) of 0.421 performed better than the MLR with lower R2 value of 0.552 and higher MAPE of 0.810 in predicting the number of home-based trips generated in the study area. The study demonstrated the higher accuracy of the RBFNN in producing trip generati...

Transportation Research Record: Journal of the Transportation Research Board, 2012

The main objective of this study is to examine the performance of the MORPC trip-based and tour-based frameworks in the context of three specific projects started and completed within the past 20 years in the Columbus metropolitan area. Regional-and project-level comparisons of the performance of the trip-based and tour-based models are made for three scenario years: 1990, 2000 and 2005. The regional-level analysis is undertaken in the context of four travel dimensions based on data availability and observed data to model output compatibility. These four dimensions are vehicle ownership, work flow distributions, work flow distribution by time-ofday, and average work trip travel times. The tour-based model performs better overall than the trip-based model for all these four dimensions. The project-level comparative assessment of the predicted link volumes from the trip-based and the tour-based models is undertaken with respect to the observed link counts and by roadway functional class. The results did not show any clear trends in terms of performance of the models by functional class or year. (MORPC) is one of the agencies that adopted a fully operational tour-based model, for the Columbus region. Subsequently, the Ohio Department of Transportation (ODOT) developed a parallel traditional trip-based model from the same data as used for the tour-based model for use in a research study. This presence of both a trip-based and a fully operational tour-based model provides a unique opportunity to test and compare the models for their policy sensitivity and forecasting ability. Accordingly, the main objective of this paper is to examine and compare the performance of the MORPC trip-based and tour-based frameworks in the context of specific highway projects. Toward this end, the current paper presents an analysis and assessment of the accuracy of predicted travel patterns by the trip-based and the tour-based models of MORPC before and after several highway projects.

Transportation Research Part B: Methodological, 2000

This study compares the performance of multilayer perceptron neural networks and maximum-likelihood doubly-constrained models for commuter trip distribution. Our experiments produce overwhelming evidence at variance with the existing literature that the predictive accuracy of neural network spatial interaction models is inferior to that of maximum-likelihood doubly-constrained models with an exponential function of distance decay. The study points to several likely causes of neural network underperformance, including model non-transferability, insucient ability to generalize, and reliance on sigmoid activation functions, and their inductive nature. It is concluded that current perceptron neural networks do not provide an appropriate modeling approach to forecasting trip distribution over a planning horizon for which distribution predictors (number of workers, number of residents, commuting distance) are beyond their base-year domain of de®nition. Ó