Refereed Journals

The nature of paratransit services makes for increased uncertainty in trip time, leading to reported unreliability and dissatisfaction by the users. While providing travel information has proved helpful in formal bus services and has been recommended for paratransit setup, little is reported about efforts at providing information to paratransit users. This study focused on one strand of possible travel information that can be provided – Travel Time. An artificial neural network (ANN)-based model was developed to predict paratransit travel times, geared towards providing information to improve user experiences. The developed model was tested on a real-world paratransit bus route (minibus taxi) in Kumasi. A travel time survey that employed a mobile phone application was used to collect data onboard the vehicles on the study route. Two ANN models were trained. The first used only historical datasets, while the second incorporated real-time information. The results show that the model in which real-time information was included performed better than that trained with only historical data. The developed models were compared with a historical average model and a regression-based model, and the results showed that the ANN models outperformed the others. The study showed that the nature of paratransit services and the limitations of continuous data collection, notwithstanding, travel times of paratransit trips can be predicted to a reasonable level of accuracy, as can be relied upon in providing information to the users.

In this study, pedestrians’ violation rate at signalised crosswalks and the associated risk factors were investigated. The data for the study were obtained through roadside pedestrian observational surveys at 10 selected crosswalks within Accra Metropolis, Ghana. The associated risk factors for the red-light violations were determined using mixed-effect logistic regression model. The descriptive statistics revealed that the red-light violation rate in the Accra metropolis was approximately 62%. Most (63.5%) of the violations occurred during the evening and on weekends (73.6%). Over 98% of pedestrians demonstrated safety consciousness by way of crossing behaviour before and during crossing by observing oncoming traffic. From the mixed-effect logistics regression model, six independent variables being age, signal cycle length, number of pedestrians crossing at a time, day of the week and pedestrian light observation significantly influenced the risk of red-light violation by pedestrians. Effective law enforcement, education campaigns and engineering measures could be used to reduce the tendency of red-light violations by pedestrians and improve pedestrian safety at signalised crosswalks in the Accra metropolis.

Paratransit users have reportedly been unsatisfied with the quality of service that they receive. Efforts at replacing the service or formalizing operations to meet users’ mobility needs have faced challenges or outrightly resisted. Approaches such as providing travel information and deploying interventions along the roadway infrastructure where the government has authority have been suggested. Deploying any of these approaches will require insights from empirical data. The study considered a key measure of service quality to users and operators alike – travel time. It investigated factors affecting the travel time of paratransit at the route and segment levels. A travel time survey that employed a mobile app (Trands) onboard paratransit vehicle was used to collect travel time, stop, and other related information on a selected route. The backward stepwise regression technique was used to determine factors affecting paratransit travel were. Dwell time, signal delay, recurrent congestion index (RCI), non-trip stops, and deviation from route were significant variables at the route level. All the factors affecting segment travel were also part of those involving route travel time except the segment length. Interestingly, deviation from the route increased overall travel time, which is against its logic. Insights gained from the study were used in suggesting proposals that can reduce travel time and improve the service quality of paratransit.

Ghana exemplifies the contribution of road crashes to mortality and morbidity in Africa, partly due to a growing population and increasing car ownership, where fatalities have increased by 12 to 15 % annually since 2008 (National Road Safety Authority (NRSA), 2017). The study described in this paper focused on understanding driver behavior at unsignalized junctions in the Ashanti Region of Ghana. Understanding driver behavior at unsignalized junctions is particularly important since failure to stop or yield can seriously affect vulnerable road users.

The study’s objectives were to develop relationships between driver behavior and junction characteristics. Understanding the characteristics that lead to determining what factors influence a driver’s behavioral response at rural junctions provides information for policy makers to determine the best strategies to address these behaviors. The study evaluated stopping behavior at rural junctions. Driver behavior was extracted from video views of ten junctions in the Ashanti Region of Ghana. A total of 3,420 vehicles were observed across all ten junctions during data collection before any analysis was conducted. The type of stop was selected as a surrogate measure of safety.

Logistic regression was used to model stopping behavior at the selected junctions. The analysis showed drivers were more likely to stop when going straight (versus a left turn) and left turning vehicles were more likely to stop than right turning vehicles. Additionally, single unit trucks and tro-tros were more likely to stop than other vehicle types. Drivers were also much more likely to stop when channelization, intersection lighting, or speed humps were present. Drivers at junctions with 4-approaches were also more likely to stop than those with 3 approaches.

The results from this research contribute valuable information about what factors contribute to positive safety behaviors at rural junctions. This provides guidance for safety professionals to select solutions and can be a valuable tool to predict the economical effectiveness of solutions to addressing junction safety in low- and middle-income countries (LMIC) such as Ghana. The results can also provide insight and recommendations to Ghanaian road safety agencies and launch sustainable efforts to raise community awareness toward decreasing road crash fatalities in Ghana.