This paper focuses on modeling transportation mode choice for commuting to work and school. The study employs a Multinomial Logit (MNL) model to examine how individuals choose their modes of transportation for work and school trips in Hawassa city. Additionally, the study aims to predict both the current and future distribution of transportation modes. To construct the model, surveys were distributed across the city's seven sub-cities, involving inspections of workplace and school travel. Primary data were collected through site visits to key transportation hubs, and travel cost data were obtained from the city's Road and Transportation Bureau. The data used for the MNL model describe the travel behaviors of employees and students, and these behaviors are integrated into the statistical analysis to formulate utility functions. The choice of transportation mode for a trip is treated as the dependent variable, while independent variables include factors like out-of-vehicle travel time, in-vehicle travel time, and travel cost. The model's validity and accuracy were assessed by examining the direction of parameter signs and comparing them to the fundamental properties of the MNL model. The study revealed that factors such as average monthly income, in-vehicle travel time, out-of-vehicle travel time, total travel cost, and comfort during the journey significantly influence individuals' choices of transportation modes. The results from the travel behavior forecast, which examines how employees choose their transportation modes, highlight the pressing need for implementing effective policy measures to incentivize the adoption of more sustainable transportation modes and promote a modal shift. Without such measures, employees are likely to increasingly favor motorcycles as their preferred mode of transportation, potentially exacerbating issues related to fuel consumption and congestion. It's evident that students tend to favor public transportation over motorcycles when selecting their mode of travel. The findings of this study offer valuable insights to decision-makers and transportation planners, shedding light on the critical factors influencing travel patterns, as well as providing estimates of existing and future market shares. These findings can serve as a foundation for crafting targeted policy adjustments to encourage sustainable transportation choices in a comprehensive manner.

Automated vehicles (AVs) are likely to bring paradigm shift in the future of transportation and in the transformation of urban space as they could reduce traffic accidents, energy consumption, and pollution while also lowering congestion expenses. To provide meaningful insights, there is a substantial need for investigation into the macroscopic evaluation of various evolutions of AVs using several measures of effectiveness. The main focus of this study is to evaluate the macroscopic operational impacts of AVs in terms of their driving logics, automation levels, and roadway type, all of which are adopted based on their passenger car unit (PCU) factors at various penetration rates, in order to assess the coexistence of AVs with heterogeneous traffic. The daily vehicle hours travelled, daily vehicle kilometers travelled, sum of delays on links, speed variation, and sum of vehicle volumes on links are used as measures of effectiveness parameters based on outputs of PTV Visum scenario manager. The results of the various scenario combinations depicted an overall improvement with advancement of driving logics, automation levels, and roadway types for each studied parameter. For instance, for better roadway condition with the motorway scenario and at higher AV penetration, the cautious driving behavior negatively affects the network performance, whereas favorable improvements are observed for the parameters of the normal and aggressive driving behaviors. Decision makers could make use of the insights obtained from the results to further shape the AV deployment aspects and extend the study considering infrastructure AV-readiness along with AV communication systems.

The growing interest in utilizing autonomous vehicles (AVs) for public transportation is increasingly evident in urban areas, and numerous locations worldwide have set up demonstration sites to test autonomous shuttles (ASs). However, there is a significant necessity to investigate the future traffic operational performance resulting from such services, in addition to the user behavior-based studies conducted during pilot projects. In this paper, an experimental approach is presented using a microsimulation environment to develop the traffic model with dedicated AS lanes. The approach examines different combinations of conventional vehicles (CVs), ASs, AV driving logics, and geometric modifications required to accommodate the AS service. The traffic performance of the network is evaluated using five measures of effectiveness (MoEs) at various market penetration rates (MPRs). The results reveal that the cautious driving behavior poses challenges primarily during the early stages of the MPR, and the same holds true at very high MPRs or if this driving behavior is sustained for an extended period. Thus, it is advisable to transition towards adopting higher driving logics (Normal and Aggressive), which exhibit highly promising results in meeting expectations. The trend of the results clearly indicates that utilizing dedicated lanes for AS buses is a potential option for integrating the emerging mobility solution into the current transportation system. The findings of this study are of significant importance, as they offer valuable quantitative insights that enable key stakeholders to make well-informed decisions on AS deployment.

The movement of the light rail vehicles (LRVs) is highly interrupted at level crossings during peak hour times, especially when the intersections are not regulated by a coordinated signal system. Traffic modelling ensures better understanding and interpretation of complex traffic interactions. This study is aimed at modelling light rail transit (LRT) system operational characteristics at level crossings in Addis Ababa City using VISSIM software. The studied scenarios at Sebategna (All Way Stop Controlled, AWSC) and CMC (Roundabout) level crossings are the baseline without LRT scenario, the actual scenario with collected LRV headways, twice arrival frequency scenario, signalized actual LRV arrival, and signalized twice actual LRV arrival. The relative comparisons among the tested scenarios depicted that significant travel time savings can be achieved in some approaches if more green time is offered to nonconflicting phases during a light rail crossing. Overall, the average additional delays at level crossings increase from the base scenario with increasing light rail crossing frequencies, and delay at the level crossing is the second important variable that contributes to the variability of train travel time at peak hours. If it is a must for an intersection to have a rail road to pass through the median, different options should be verified based on the trade‐off between the operational cost of the level crossing and the cost incurred if it is grade separated.

One of the primary causes of poor public transport performance is delays at intersections. Among the efficient and sustainable solutions to boost mass transportation performance, Bus Rapid Transit (BRT) consists of infrastructures integrating dedicated bus lanes and smart operational service with different ITS technologies like Transit Signal Priority (TSP). This research studies the competence of buses operating on junctions of the BRT corridor where they have Signal Priority on the dedicated lane. The studied intersection is located around the center of the Addis Ababa BRT-B2 line, which is relatively gentle grade and characterized by the high traffic and pedestrian volume. Microscopic models were created for the chosen intersection, along with possible calibration and validation; moreover, a statistical comparison was performed to evaluate different scenarios with the goal of displaying the deployment benefits. To assess the performance of BRT buses and their overall influence on general traffic, scenarios with and without TSP were evaluated. PTV VISSIM and the VisVAP add-on simulation program were used to examine TSP alternatives. Incorporating TSP reduced the travel time by up to 4.78% in the priority direction, the average travel speed increased by 7.25%, and the queue length also reduced by a maximum of 6%, whereas in the non-priority direction, the queue length increased by a maximum of 2.5%. Moreover, the overall average passenger delay has reduced by an average amount of 15%. One of the simplest ways to improve transit performance could be signal priority strategies, which has a minor influence on the general traffic.