No abstract

Evaluation of service quality (SQ) based on user preferences has become a primary concern for the transportation authorities. The most significant attributes of public transportation systems are revealed through service quality analysis. This information serves as valuable input in constantly updating the quality of public transportation services. An appropriate tool is therefore mandatory in this regard.

This study applies probabilistic neural network (PNN) and adaptive neuro fuzzy inference system (ANFIS) to develop bus service quality (SQ) prediction model based on the preferences stated by users (on a scale of 1 to 5). A questionnaire survey is conducted and a data set from the survey is prepared to develop the SQ prediction model using PNN and ANFIS. Results show that ANFIS produced better prediction than PNN. The research is further extended to include ranking of the SQ attributes according to their impact on the overall result from the developed model. Attributes such as punctuality and reliability, seat availability, and service frequency were found to be the top three attributes that mostly affect the decision making process of the users. This study can aid service providers in improving the most important attributes of bus service to develop the quality of service, thereby increasing transit ridership.

This study deals with the reconstruction of vehicle trajectories incorporating a data fusion framework that combines video and probe sensor data in heterogeneous traffic conditions. The framework is based on the application of variational formulation (VF) of kinematic waves for multiple lane conditions. The VF requires cumulative count and reference trajectory as boundary conditions. The VF also requires generation of lopsided network using fundamental diagram (FD) parameters. In this regard, cumulative count and FD parameters are obtained from the video sensor, while reference vehicle trajectory is obtained from the probe sensor. The analysis shows that the framework can provide an accuracy of 83% in trajectory estimation from the nearest reference trajectory. However, the accuracy decreases as the reference trajectory gets farther away from the estimated one. Additionally, an extension of the VF to accommodate roadway side friction is presented. The FD as well as lopsided network reform when the roadway capacity varies due to side friction. Consequently, the vehicle trajectory bends to accommodate the capacity fluctuation.

In this paper, five pedestrian level of service (PLOS) methods are outlined in brief with respect to their assets and their limitations: (a) the Australian method, (b) the Highway Capacity Manual 2010 method, (c) the trip quality method, (d) the Landis method, and (e) the Tan Dandan method. In this study, each method was implemented to consider its suitability for use in Dhaka City, Bangladesh, through the integration of objective measurement and subjective assessment. The objective measurement consisted of a determination of the PLOS of five study locations in Dhaka City and the adoption of field data on traffic, geometric, and environmental factors. The subjective assessment had its basis in a user perception rating by 50 individuals of the service quality of pedestrian facilities in the selected study areas. A separate survey of 415 individuals was conducted to identify the most favored of 25 service quality attributes extracted from the five PLOS methods. The perception ratings were scrutinized to identify any potential deviations that arose from participant age and gender. In the ratings, the Australian method prevailed over the other four methods with a score of 18. The trip quality method scored second best with 16 points. The separate survey substantiated the adequacy of the Australian method for use in Dhaka City and included seven of the eight most desired and popular PLOS attributes selected by the survey respondents. Future research should be devoted to the development of a new PLOS method that uses the factors identified in this paper.