Yaser E. Hawas

Evaluating and Enhancing the Operational Performance of Public Bus Systems Using GIS-based Data Envelopment Analysis

In this paper, the baseline performance level of Al Ain Public Bus Service is evaluated using Data Envelopment Analysis (DEA) based on some selected input (travel time per round trip, total number of stops, total number of operators, total number of buses) and output (daily ridership and vehicle-kilometer) variables. Two types of scenarios were developed and tested. The first set of scenarios aimed at investigating the possibility of reducing the operating cost while maintaining the same performance levels (efficiency and effectiveness) for the routes. The second set of scenarios was used to demonstrate how the baseline performance levels can be improved by slightly altering the route alignment (and subsequently input and output variables). Sensitivity analysis was then conducted to measure the efficiency and effectiveness of each route. Conclusions on how the transit authority can reduce daily operating hours while maintaining the existing performance level are made. Also, suggestions are presented on how to improve the overall performance level of the bus service by changing some route characteristics.

A Microscopic Simulation Model for Incident Modeling in Urban Networks

Abstract This paper reviews the main modules of an integrated system for incident management in real-time, -sim. A core to such a system is a microscopic simulator with extended abilities to model the temporal and spatial evolution of specified non-recurrent traffic conditions. The paper reviews the mathematical formulation of the car-following and lane-changing modules. The model is validated using a simulation-based approach. Concluding comments on the general validation process of the model are provided. The paper finally presents a sample of the accident patterns replicated by the model together with their implications for real world validation.

Autonomous real time route guidance in inter-vehicular communication urban networks

This paper presents an inter-vehicular communication (IVC)-based algorithm for real-time route guidance in urban traffic networks. The algorithm enables communication between searcher vehicle and candidate vehicles whose origin node matches the destination node of the searcher vehicle, and traveling in the opposite direction. The data entities of knowledge sharing among the vehicles and the algorithmic procedure as well as the conditions for information sharing are presented in detail. The mathematical formulation of the procedure is also presented. A microscopic simulation model is utilized to assess the effectiveness of the algorithm against the benchmark shortest path algorithm. Average travel time, and overall network productivity are presented to measure the effectiveness of the algorithm. Simulation runs are conducted under various network congestion levels, link speeds and link lengths in order to evaluate the network productivity, and the travel time measures of the presented IVC algorithm compared to the benchmark shortest path algorithm, decentralized route guidance systems as well as other IVC-based algorithms.

An integrated simulation-based fuzzy logic model for real-time traffic signal control

Fuzzy logic has been recognised in the literature as an effective methodology for real-time signal control. The majority of the fuzzy controllers in the literature depend on simple logic which in turn depends on the raw data of a single detector. Their input variables are usually simple estimates of traffic measures such as flow, speed or occupancy, estimated from such single-detector readings. A room for improvement is sought in this article by developing a fuzzy logic model (FLM) that could be integrated with smarter ‘processing’ tools to estimate several traffic measures from multiple detectors on each approach. The estimates obtained from this processing tool are integrated as input knowledge into the FLM. The mathematical formulation of these traffic measures is presented. The fuzzy logic structure is addressed in detail. A simulation model is devised to test the effectiveness of the FLM. The results are presented and discussed in depth.

A multi-stage procedure for validating microscopic traffic simulation models

Abstract Validating microscopic traffic simulation models incorporates several challenges because of the inadequacy and rareness of validation data, and the complexity of the car following and lane-changing processes. In addition, validation data were usually measured in aggregate form at the link level and not at the level of the individual vehicle. The majority of model validation attempts in the literature use average link measurements of traffic characteristics. However, validation techniques based on averages of traffic variables have several limitations including possible inconsistency between the field observed and simulation-estimated variables, and as such the resulting spatial–temporal traffic stream patterns. Due to these inconsistencies, this paper introduces a novel approach to the validation of microscopic traffic simulation models. A three-stage procedure for validating microscopic simulation models is presented. The paper describes the field measurements, experimental setup, and the simulation-based analysis of the three stages. The purpose of the first stage is to validate a benchmark simulator (NETSIM) using limited field data. The second stage examines the spatial–temporal traffic patterns extracted from the benchmark simulator versus those extracted from the simulation model to be validated (I-SIM-S). Different traffic patterns were examined accounting for various factors, such as traffic flow, link speeds, and signal timing. The third stage compares the aggregate traffic measures extracted from the subject simulator against those extracted from the benchmark simulator.

Developing fuzzy route choice models using neural nets

The paper discusses the calibration methodology of a neuro-fuzzy logic for route choice behaviour modelling. Neuro-fuzzy refers to the trend of logics that couple the traditional fuzzy logic structure with neural nets training capabilities for knowledge base and parameters settings. The fuzzy logic accounts for the various factors of potential effect on the route choice utility perceived by the traveller. The structure of the fuzzy logic, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model.

A Histogram-Based Model for Road Traffic Characterization in VANET

This paper presents a new route guidance algorithm and a compact road traffic model that can be easily obtained and transmitted in real-time by individual vehicles while they are travelling on streets or queuing in road cross junctions. The proposed algorithm uses histograms as the network traffic model that captures the arrival rate distribution in VANET. In addition, the paper presents an analysis method that works directly with the histogram model to obtain the queue occupancy distribution at cross-junctions or traffic signals using a finite queue model. A microscopic simulation model is utilized to assess the effectiveness of the traffic model in detecting traffic congestion and directing vehicles to choose better paths. Results show that the proposed road traffic model provides a good prediction of road traffic status, and can be used in conjunction with any standard shortest path algorithms to provide an efficient mechanism for selecting fastest road path.

Transportation infrastructure development in the UAE: Stakeholder perspectives on management practice

Purpose – The purpose of this paper is to examine the major management issues that impact on mega transportation infrastructure projects in the United Arab Emirates (UAE) and identify the factors that cause unsuccessful project completions. The paper further seeks to identify the changes that can be made to improve project success.Design/methodology/approach – This is a qualitative study that involved face‐to‐face interviews with 20 key experienced transportation construction stakeholders who had been involved in a number of different projects in the UAE. This was followed by a focus group discussion involving ten key stakeholders who had been involved in the construction of a mega project – the Dubai Fujairah Highway. Analysis of the interview data was conducted using NVivo.Findings – The findings highlight the complexity involved in managing mega transportation infrastructure projects in the UAE. Multiple stakeholders (government agencies, sponsors/clients, management firms, consultants and contractors)...

HBA-histogram based algorithm for real time route forecasting in urban area

Intelligent Transport System (ITS) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous traffic system, while providing communication access to vehicles with various Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication for the development of a safety commuting service. Building a route forecasting system is a complex task, mainly because of the changing traffic conditions. This paper presents a new route guidance algorithm and presents a compact road traffic model with real time updating. The algorithm enables to select a shortest path between source to destination by estimating the histogram model which captures the higher order distribution function using ITS. The data entity collection through sensors used for histogram modelling is presented in detail. A microscopic simulation model is utilized to evaluate the effectiveness of the proposed traffic model against the existing algorithm. Average travel time and overall network productivity are measured using the simulation. Various scenarios were created to study the impact of the proposed model in the traffic modelling.

A non-cooperative neuro-fuzzy system for integrating ATIS and ATMS decisions

This paper addresses the problem of integrating successful existing implementations of Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS). The methodologies and challenges to integrate ATIS and ATMS are addressed in details. This paper discusses in details the development of a rule-based neuro-fuzzy logic to integrate existing ATIS and ATMS when they operate in a non-cooperative manner. The individual existing logics are initially upgraded to the so-called ATIS and ATMS stand-alone systems, which may be regarded as bi-level optimization systems. The upper level (of the bi-level optimization system) represents an augmented process for guessing the counter systems decisions, and the lower level represents the existing logic (of ATIS or ATMS). The stand-alone systems are then replicated by simulation-based optimization algorithms, which are used to generate the training data necessary to calibrate the neuro-fuzzy logic. The role of the neural nets and the methodology of fuzzy-logic calibration are discussed in details. The effectiveness and robustness (of the neuro-fuzzy integrated ATIS/ATMS system) are assessed using simulation-based experiments with two different hypothetical networks.