The accurate depiction of the existing traffic state on a road network is essential in reducing congestion and delays at signalized intersections. The existing literature in the optimization of signal timings either utilizes prediction of traffic state from traffic flow models or limited real-time measurements available from sensors. Prediction of traffic state based on historic data cannot represent the dynamics of change in traffic demand or network capacity. Similarly, data obtained from limited point sensors in a network provides estimates which contain errors. A reliable estimate of existing traffic state is, therefore, necessary to obtain signal timings which are based on the existing condition of traffic on the network. This research proposes a framework which utilizes estimates of traffic flows and travel times based on real-time estimated traffic state for obtaining optimal signal timings. The prediction of traffic state from the cell transmission model (CTM) and measurements from traffic sensors are combined in the recursive algorithm of extended Kalman filter (EKF) to obtain a reliable estimate of existing traffic state. The estimate of traffic state obtained from the CTM-EKF model is utilized in the optimization of signal timings using genetic algorithm (GA) in the proposed CTM-EKF-GA framework. The proposed framework is applied to a synthetic signalized intersection and the results are compared with a model-based optimal solution and simulated reality. The optimal delay estimated by CTM-EKF-GA framework is only 0.6% higher than the perfect solution, whereas the delay estimated by CTM-GA model is 12.9% higher than the perfect solution.

Traffic streams in many developing countries consist of various modes of transport, with high heterogeneity in driver behavior. Modeling these types of traffic streams, in which traffic rules (speed limit, lane discipline, etc.) are not strictly followed, is a complex task. A review of the existing literature shows that there is a lack of traffic flow models that model the behavior of heterogeneous and undisciplined traffic streams. Like other undisciplined traffic streams, there are no speed limits (hence no speed enforcement) on most of the roads in Karachi, Pakistan. Lane discipline is also not observed by drivers, which results in a varying number of traffic lanes on a road. Therefore, most of the existing traffic flow models/simulation packages developed for disciplined traffic streams cannot appropriately model traffic streams without lane discipline. This research proposes a width-based cell transmission model (WCTM) by developing a fundamental flow-density diagram whose parameters are a function of the road width. Extensive field data have been collected from a selected arterial in Karachi for development of the fundamental traffic flow diagram. The values of the computed parameters are significantly different than the values reported in the literature. The piecewise-linear flow-density relation is developed by optimally estimating the breakpoints. Results show that the quadrilateral and pentagonal-shaped fundamental diagrams fit better with the collected data in comparison with the triangular-shaped fundamental diagram. The proposed WCTM is applied to selected segments of an arterial and results show that the WCTM was able to accurately model different traffic conditions.

Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.

This study presents a comparative analysis of selected parameters of existing formal and informal Public Transport (PT) modes being operated on three different types of fuels. The performance of PT modes is analysed on fuel consumption, capacity, transportation cost, and emissions resulting from these modes. The required data were collected using route-check survey method and conducting a questionnaire-based survey from drivers and operators of these modes. Furthermore, the performance of proposed buses for an under-construction BRT corridor is also evaluated and compared with the existing PT modes. The comparative analysis of the existing PT modes in Karachi shows that CNG operated PT modes are economically more efficient, which caused the conversion of diesel engines of buses and minibuses to CNG fuelled-engines. The study, for the first time, evaluates and compares the performance of informal PT mode (chingchi) with other modes of PT. Results show that the PT modes with less capacity, such as chingchi, should be discouraged due to their comparatively lower performance on the selected parameters. This study can be used by the authorities to analyze the performance of existing modes and prioritize the PT modes for future planning.

Accurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using intelligent transportation systems. Existing applications of dynamic traffic assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce an estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight the estimation of real-time traffic state are focused only on traffic state estimation and have not utilised the estimated traffic state for DTA applications. In this paper we propose a framework which utilises real-time traffic state estimate to optimise network performance during an incident through the traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using the cell transmission model (CTM) and the measurements from the traffic sensors in extended Kalman filter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on alternative routes in a small traffic network, and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments on a two-route network, the proposed estimation and information method is seen to significantly improve travel times and network performance during a traffic incident.