Amer Shalaby

Macrolevel Accident Prediction Models for Evaluating Safety of Urban Transportation Systems

A series of macrolevel prediction models that would estimate the number of accidents in planning zones in the city of Toronto, Ontario, Canada, as a function of zonal characteristics were developed. A generalized linear modeling approach was used in which negative binomial regression models were developed separately for total accidents and for severe (fatal and nonfatal injury) accidents as a function of socio-economic and demographic, traffic demand, and network data variables. The variables that had significant effects on accident occurrence were the number of households, the number of major road kilometers, the number of vehicle kilometers traveled, intersection density, posted speed, and volume-capacity ratio. The geographic weighted regression approach was used to test spatial variations in the estimated parameters from zone to zone. Mixed results were obtained from that analysis.

A Trip Reconstruction Tool for GPS-based Personal Travel Surveys

ABSTRACT This article reports on the development of a trip reconstruction software tool for use in GPS-based personal travel surveys. Specifically, the tool enables the automatic processing of GPS traces of individual survey respondents in order to identify the road links traveled and modes used by each respondent for individual trips. Identifying the links is based on a conventional GIS-based map-matching algorithm and identifying the modes is a rule-based algorithm using attributes of four modes (walk, bicycle, bus and passenger-car). The tool was evaluated using GPS travel data collected for the study and a multi-modal transportation network model of downtown Toronto. The results show that the tool correctly detected about 79% of all links traveled and 92% of all trip modes.

Development of planning level transportation safety tools using Geographically Weighted Poisson Regression

A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models.

The Four Pillars of Sustainable Urban Transportation

The unsustainable nature of current urban transportation and land use is well recognized. What is less clear is the prescription for how to move towards a more sustainable future, especially given the many interest groups involved, the complexity of urban systems and the fragmented nature of decision‐making in most urban regions. It is argued that the process of achieving more sustainable transportation requires suitable establishment of four pillars: effective governance of land use and transportation; fair, efficient, stable funding; strategic infrastructure investments; and attention to neighbourhood design. A review of each pillar identifies key issues. The characteristics of an ideal body for governance of land use and transportation are considered. Trade‐offs are identified with: spatial representation; organizational structure; democracy; and market philosophy. Effective financing and pricing of urban transportation may be distorted because responsibility for infrastructure is separated from service provision. Financing mechanisms are categorized depending on vehicle use and location. Investment in infrastructure for alternative fuel vehicles and intermediate semi‐rapid transit may be required in many cities. Major investment in public transit infrastructure will likely not suffice if macro land use and micro neighbourhood designs are not supportive of these investments.

Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems

This project developed an integrated Global Positioning System-geographic information system (GPS-GIS) to automate the processing of GPS-based personal travel survey data. Two versions of the analysis system were developed in this project: a GPS-alone system, which uses only GPS travel data as input, and a GPS-GIS integrated system, which uses both GPS travel data and topologic information on GIS platform as input. The GPS-alone system includes an activity identification algorithm and a fuzzy logic-based mode identification algorithm. The GPS-GIS integrated system includes link identification on a GIS platform as well as an interactive link matching-mode identification subsystem, which further refines the results from previous identifications performed separately. This project demonstrates how GPS travel data analysis can be automated and highlights the benefits brought by an interactive analysis system, providing an innovative analysis method for personal-based GPS multimodal travel surveys.

Safety Prediction Models: Proactive Tool for Safety Evaluation in Urban Transportation Planning Applications

Urban transportation planning has traditionally focused on capacity and congestion issues with some attention paid to operation and management and with the treatment of such issues typically made proactively. In contrast, road safety has received little attention in the planning process. Safety-conscious planning is a new proactive approach that incorporates safety issues into the transportation planning process. This approach requires a safety planning decision-support tool to facilitate a proactive approach to the assessment of safety implications of alternative network planning initiatives and scenarios. The objective of this research study is to develop a series of zonal-level collision prediction models that are consistent with conventional models commonly used for urban transportation planning. A generalized linear regression modeling approach with the assumption of a negative binomial error structure was employed for exploring relationships between collision frequency in a planning zone and some explanatory variables such as traffic intensity, socioeconomic and demographic factors, land use, and traffic demand measures. Planning-level safety models developed in this study with data for the city of Toronto, Canada, are presented with illustrative applications of how they can be used as decision-support tools for planners to explicitly consider safety in the transportation planning process. Macrolevel collision modification factors are presented to illustrate how the models can be used to examine the impact of each individual planning variable on the safety of an urban zone.

Success and Challenges in Modernizing Streetcar Systems: Experiences in Melbourne, Australia, and Toronto, Canada

On-street running in mixed traffic has been identified as the least desirable right-of-way for light rail and tram systems. While most cities in the developed world have withdrawn streetcar systems, substantial networks have been retained in Melbourne, Australia, and Toronto, Canada. Although some commentators have seen the retention of these systems as visionary, there are substantial challenges to be faced in addressing conflicts between streetcars and rising road traffic. Poor running speeds, unreliability, safety, and difficulties in providing universal access are significant issues for modern streetcar systems. Experiences are described in regard to planning and operating the Melbourne and Toronto streetcar systems. The types of challenges being faced in providing services are contrasted. Programs to address the challenge of creating modern high-quality transit systems out of streetcars are compared. Finally, success strategies in modernizing streetcar systems are identified.

Access Walking Distance, Transit Use, and Transit-Oriented Development in North York City Center, Toronto, Canada

This study had two main objectives: to examine how variations in walking distance to rapid transit are related to mode choice as well as to auto ownership and use and to investigate whether temporal changes in the built environment associated transit-oriented development in close proximity to rapid transit (subway) service encourage residents to use transit. The city of Toronto, Canada, and one of the fastest-growing suburban centers of Toronto–-the North York City Center located near the northern edge of Toronto on the Yonge subway line–-were selected as case studies. With the 2001 Transportation Tomorrow Survey (TTS) data, a quantitative analysis was employed; it that focused on home-based trips by using the citys subway network to demonstrate how walk-access distances to rapid transit are related to subway mode share, auto ownership, and auto use. Further analysis was undertaken to examine the temporal changes in land use and related travel behavior over a 15-year period by comparing TTS data for 1986 and 2001. The results of the analyses illustrate quantitatively the strong association between convenient walk access, lifestyle, and transit use, not only during peak hours but also throughout the day. The results show how the promotion of focused development within a convenient walking distance of rapid transit service in a relatively low-density suburb of Toronto has, over 15 years, been accompanied by a substantial shift in residents’ travel behavior toward increased transit use.

Advanced Transit Signal Priority Control with Online Microsimulation-Based Transit Prediction Model

An advanced transit signal priority (TSP) control method is presented: it provides priority operation in response to real-time traffic and transit conditions. A high-performance online microscopic simulation model was developed for the purpose of predicting transit travel time along an intersection approach. The proposed method was evaluated through application to a hypothetical intersection with a nearside bus stop. The performance of the proposed method was compared with that of normal signal operation without TSP and a conventional signal priority method. The experimental results indicated that the developed method provided efficient and effective priority operation for both transit vehicles and automobiles. The proposed method significantly reduced transit vehicle delays as well as side-street traffic delay compared with conventional active priority control.

Real-Time Optimization for Adaptive Traffic Signal Control Using Genetic Algorithms

Control methodologies of traffic signals have significantly improved during the recent past along with advancements in technology. Adaptive traffic signal control is the most recent and advanced control type of traffic signals. Adaptive control is able to efficiently relieve traffic congestion by continuously adjusting signal timings according to real-time traffic conditions. Conventional optimization methods such as integer programming, hill climbing, or descent gradient searching have been gradually overshadowed by genetic algorithms in many areas including traffic signal operation. The research presented in this article is distinct from previous studies in that it focuses on real-time adaptive signal optimization using genetic algorithms. The proposed adaptive signal system provides acyclic signal operation based on a rolling horizon real-time control approach. The algorithm was tested using microsimulation for on-line evaluation and comparison to fixed-time plans generated from the latest TRANSYT-7F version 9.7, which has a genetic optimization feature. The developed signal system consists of three major components including a genetic algorithm optimization module, an internal traffic simulation module, and a database management system all working in cooperation to optimize signal timings in real time. Using the pseudo on-line simulation platform, three testing scenarios for high, medium, and low level of traffic demands were conducted focusing on evaluating several important features of the proposed adaptive signal control system. The test results indicated that real-time genetic control outperformed fixed-signal timing plan in all scenarios based on total vehicle delay.