Sustainable management of traffic flows at signalized intersections is an important issue in terms of traffic engineering. The minimization of lost time, emission, fuel consumption, etc., can be achieved by optimization‐based intersection management. In this study, a new traffic signal control model is developed for the management of three‐leg signalized intersections. In the proposed model, signal timing and signal phasing are optimized simultaneously using Differential Evolution (DE) algorithm which is one of the population‐based metaheuristic algorithms. The effectiveness of the model is tested on sample traffic scenarios with VISSIM simulation software considering average vehicle delay performance criteria. Results show that the proposed approach may reduce the average vehicle delay between the rates of 28%–42% and 3%–38% comparing to the optimum fixed‐time signal control and vehicle‐actuated signal control for tested scenarios, respectively.

In the last few years, there has been a significant rise in the number of private vehicles ownership, migration of people from rural areas to urban cities, and the rise in the number of under-maintained freeways; all these have added to the perennial problem of traffic congestion. Traffic flow prediction has been recognized as the solution in alleviating and reducing the problem of traffic congestion. In this research, we developed an adaptive neuro-fuzzy inference system trained by particle swarm optimization (ANFIS-PSO) by performing an evaluative performance of the model through traffic flow modelling of vehicles on five freeways ($N1,N3,N12,N14 \mathrm{and} N17)$ using South Africa Transportation System as a case study. Six hundred and fifty (650) traffic data were collected using inductive loop detectors and video cameras from the five freeways. The traffic data used for developing these models comprises traffic volume, traffic density, speed of vehicles, time, and different types of vehicles. The traffic data were divided into 70% and 30% for the training and validation of the model. The model results show a positively correlated optimal performance between the inputs and the output with a regression value $R^{2 }$ of 0.9978 and 0.9860 for the training and testing. The result of this research shows that the soft computing model ANFIS-PSO used in this research can model vehicular traffic flow on freeways. Furthermore, the evidence from this research suggests that the on-peak and off-peak hours are significant determinants of vehicular traffic flow on freeways. The modelling approach developed in this research will assist urban planners in developing practical ways to tackle traffic congestion and assist motorists and pedestrians in travel behaviour decision-making. Finally, the approach used in this study will assist transportation engineers in making constructive and safety dependent guidelines for drivers and pedestrians on freeways.

Signal timings at signalized intersections are frequently optimized by considering commonly used vehicle delay models. It is generally believed that reducing the average number of stops can also decrease the average vehicle delay. Therefore, the aim of this research is to address the question: “Can similar performance outcomes be achieved through the Minimization of Average Vehicle Delay (MAVD) and the Minimization of Average Number of Stops (MANS)?” The first phase of the study entails the creation of two distinct signal timing optimization models based on the Akcelik average vehicle delay and average number of stops models. Subsequently, scripts were developed in MATLAB to identify the optimal signal timings for both approaches utilizing the Differential Evolution Algorithm. In the third phase, 30 traffic scenarios were generated, each varying in overall traffic volumes at the intersection. Subsequently, the signal timings derived from the MAVD and MANS approaches were applied independently to these scenarios, and performance indicators (average vehicle delay and average number of stops) were compared. The results reveal that the utilization of MANS-based signal timings instead of MAVD may lead to an increase in average vehicle delays of up to 113.55%. Additionally, it is demonstrated that when MAVD-based signal timings are applied instead of MANS, the average number of stops can increase by up to 16.28%. Finally, it is concluded that as the overall traffic volume at the intersection increases, these growth rates tend to decrease.

Public transport is one of the main actors of sustainable transportation. To make an increase in the use of public transport systems and a decrease in private car usage are one of the main purposes of decision makers in many countries. Usage of public transport system has a strong relation with the quality of service. Service quality includes many parameters such as comfort, frequency, information system etc. Fare is another important parameter on demand for public transport systems. All of these parameters are varied by users' perceptions. This study focuses on public transport systems of Denizli, Turkey and investigates users' perceptions. Bus and paratransit (minibus) modes are regarded in the study. The public survey is performed for these modes. In the surveys, different questions have been asked to participants. Answer of each question is evaluated separately and distribution of each question for gender, ages and jobs are analyzed. Results obtained are summarized and discussed.

The Covid-19 pandemic has affected the entire world and changed many aspects of daily life, including transportation behaviour and preferences. This study examines the situation of transportation behaviour and preferences before and after the Covid-19 pandemic process through a survey study. A total of 471 people participated in the survey, and 30 questions were posed to participants regarding transportation modes preference, daily usage habits, public transportation usage rate, income status, perceived risk level of transportation systems, preferences before and after the pandemic process, and more. The answers were classified, evaluated and statistically analysed. The results showed that users were significantly affected by the Covid-19 process and changed their transportation mode preferences. The use of buses in urban journeys decreased, and the use of private vehicles significantly increased after the Covid-19 process. Hygiene was the key factor in travel, followed by the vehicle occupancy rate, ventilation, fare and travel time factors. The perceived risk levels of public transportation systems were ranked as metrobus, minibus (paratransit), bus, metro, tram and ferry. The study suggests that future transportation system designs should consider user preferences, as well as changes in education and working conditions, and pandemic conditions.