We introduced a novel approach where bus riders provide advance notice of their stops, allowing buses to skip unrequested stops and take shortcuts. This method increases stop density, reduces walking distances to and from bus stops, and maintains operational efficiency. To design this system, we developed optimization models that maximize the number of stops while adhering to tour duration and arrival time constraints. A case study in Allegany County, Maryland, demonstrates significant enhancements for routes that were both underutilized and had layouts conducive to substantial shortcuts.
Through a university-industry collaboration, we have identified (1) modernizing the optimization engine in paratransit scheduling software suites, and (2) incorporating alternative service providers in paratransit service optimization as two critical steps in overcoming some challenges in paratransit practice. We developed a nested decomposition method in which a column generation–based solution approach is embedded in a temporal decomposition framework. Additionally, we integrated alternative service types, such as accessible taxis, in paratransit scheduling and designed a reoptimization procedure. The new optimization methods were implemented in a software suite of IT Curves and deployed in paratransit operations in the Washington, DC, metro area. It was found that the improved optimization engine, relative to the legacy, led to significant improvements in key operational metrics and yielded substantial operating cost reductions (approximately 15%).
In this paper, we explored outsourcing outlier trips to transportation network companies (TNCs) in Dial-a-ride (DAR) operations, using a two-stage solution framework based on mixed integer programming and multi-vehicle improvement heuristic, which achieved cost reductions of 7%-13% for a medium-sized DAR operator in Maryland.
While modern machine learning techniques enhance the predictive accuracy of zone-to-zone direct demand models, they often lack inference ability due to complex structures and are limited by small sample sizes of fixed origin-destination (OD) pairs. To address these issues, a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) is proposed, combining linear explanatory components with non-linear patterns to improve prediction accuracy by generating additional OD data. This model has shown significant improvement in predicting OD ride-hailing demand in Chicago and Austin and allows for inference on the relationship between demand and predictor variables.
Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner to forecast demand and supply-demand gap in a ride-hailing system poses a burden for the expanding transportation network companies. To address this issue, a deep multi-task learning architecture, GESME-Net, is proposed to simultaneously forecast demand and supply-demand gap across different tasks and cities. This framework employs a gated ensemble of spatio-temporal mixture of experts for convolutional recurrent neural network (CRNN), CNN, and recurrent neural network (RNN), as well as a task adaptation layer that reveals feature contributions while learning a joint representation for multi-task learning.
Paper: doi.org/10.1016/j.multra.2024.100166
Ride-hailing companies require accurate spatio-temporal forecasting of both demand and supply-demand gap to reduce passenger waiting times, minimize driver search friction, and ensure efficient service management. However, such forecasting is challenging due to strong spatio-temporal dependencies and, in many cases, the lack of explicit spatial adjacency information arising from unplanned cities and privacy issues. To address these issues, a spatio-temporal deep learning architecture integrating a feature importance layer, a one-dimensional convolutional neural network (CNN), and a zone-distributed independently recurrent neural network (IndRNN) is introduced to handle anonymized spatial data.
Paper: doi.org/10.1049/itr2.12073
Ridesourcing is becoming a popular mode of transportation in many countries around the world, including developing countries like Bangladesh. Previous studies have mostly focused on the behavioral choice models of car ridesourcing, while the factors affecting the choice between car and motorcycle ridesourcing services remains unexplored. To better understand the influence of socio-economic and demographic features on the joint frequency of using motorcycle and car ridesourcing, this research was conducted based on the data collected through questionnaire at key locations in Dhaka city, the capital of Bangladesh. A bivariate ordered probit model was developed for the usage of motorcycle and car ridesourcing to find their respective correlation with various socio-economic and demographic factors. The outcomes indicate that people with certain types of socio-demographic background prefer to use motorcycle over car for ride-sourcing and vice-versa. The result also shows that gender, household density, income, educational qualification, smart-phone usage have a positive impact on the frequent use of ride-services for both motorbikes and cars. The outcomes are expected to provide useful insights to the ride-sourcing service providers, transport agencies and policy makers of Bangladesh in understanding the choice behaviors from the ride-sourcing user perspectives.
Walking often involves crossing a road, where the chance of vehicle-pedestrian conflict increases. Footbridges are one of the engineering instruments that offer safety for pedestrians during road crossing avoiding any physical conflicts with pedestrians. However, the usage of pedestrian bridge is affected by several factors. This paper aims to find the effect of socio-demographic factors and perception on the frequency of footbridge usage. Responses collected from 893 individuals at 10 footbridges in Dhaka are used to develop a Random Parameter Ordered Probit Model.
Water bus services can be considered as one of the means to avoid traffic congestion for cities surrounded by rivers/lakes. Bangladesh Inland Water Transport Corporation (BIWTC) launched a water bus service inside the capital Dhaka from Sadarghat to Gabtoli touching several locations through Buriganga and Turag rivers. This study aims to evaluate the satisfaction level of the passengers on the service quality and safety of the water bus service in Dhaka city. A water bus service on Hatirjheel lake connecting several areas of Dhaka city is considered as the study area for this research. The satisfaction level on the service quality and safety is measured from the responses of a questionnaire survey conducted on passengers using this route. Several multiple linear regression models are developed to correlate the respondents' socio-economic, demographic, travel characteristics and experiences with various aspects of service quality and safety issues.
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