For many decades, traffic management strategies have been dominated by cars. You may say that it is something obvious and why did you explain this to me? What can be controlled if a traffic controller does not control the cars? I am not saying that a traffic control system should not control cars, but I say they should not only be implemented to improve car-related performance. A car is a tool or a means. It is like a knife in your kitchen or a mobile app on your smartphone. Do you design your kitchen to satisfy your knife?
People are the main and the only users of transportation systems, but when cars emerged, we gradually forgot people in traffic planning. I am not even talking about a car that has an engine. Even before the invention of vehicles and during the carriage era, people were forgotten in urban planning. We made the cars so crucial that they dare kill people in the streets. The car-dominated traffic management strategies are not only the fruit of vehicle-based traffic planning, but the lack of data is another practical factor. If traffic planners wanted to count passengers on board each car instead of the cars, how could they do it? Even counting the cars is not easily possible. We could not collect car data unless they appear in a camera or pass an inductive loop.
Recently, an emerging technology allowed us to collect more information about cars. I am talking about connected vehicles (CV). Usually, when you talk about a CV, people think you mean a self/driving car that can move you without any effort, and the only thing it should know is your destination. However, here I only tell a car that can send data to the receiver at any time and at any point. You can also call it a probe car. A CV does not need to pass a specific topic to appear in the traffic management database. It can be covered at any location in a city. This is a game-changer ability. In the past, all traffic controllers tried to facilitate vehicle movement at signalized intersections based on the data they collected from a stationary data collection. In this case, the controllers cannot predict the arrival time of cars at the intersection and cannot implement compelling adaptive scenarios to consider real-time traffic flow.
Probably, it happened that you forgot to wear your seat belt in your car. Your car instantly knows that and warns you to wear it. How does your car know this? Defiantly by the sensor. It means your vehicle can know how many passengers are in your car, and if you have a CV, then this data can be transmitted to a traffic controller. What if the traffic controller runs a strategy based on the counted passengers of all cars, not the vehicles? I call this a user-based traffic control strategy. In a user-based traffic control strategy, the main objective of the control is to provide priority for cars based on the number of users on board. Accordingly, a bus is prioritized over a car, and a vehicle with four users on board is prioritized over a single occupancy car.
Another benefit of collecting users’ data is using it in the social impact evaluation of traffic control strategy, which allows us to improve city sustainability indicators. To evaluate social impact, we need to calculate the total social cost for each user, consisting of several expenses such as travel time, fuel consumption, and emission costs. All these mentioned costs are calculated for each user separately, and then we can know how a traffic strategy can affect the total social cost.
In my Ph.D. thesis, I developed a user-based traffic control strategy to discover the benefits of a user-based traffic management strategy based on numerical evidence. In my traffic control system, first, connected cars are detected, and the required data consisting of car data and users’ data is collected. In addition, I need the duration of each green phase. Then, using a mathematical model, I predict the arrival time of cars at the intersection. Now, I have a model that can predict cars' arrival time based on green times. Accordingly, I used a genetic algorithm optimization to find the optimum green time durations to maximize user throughput and minimize user delay in the intersection.
I tested this method in traffic simulation software and measured the traffic control's performance. I compared the result to a baseline traffic control. The result was promising. I could prove that a user-based traffic control can reduce user delay and prioritize cars based on the number of users on board. In another research, I also measured the social cost of a user-based traffic control strategy and a baseline traffic control. For this purpose, I simulate two real intersections in Helsinki in a traffic simulator. The result proved that user-based traffic control can also effectively improve social impacts.
All in all, I can state that when new technologies are emerging, we should rethink about we have developed based on the limitation we had before. If these new technologies can give us new opportunities, we should use them to build better and greener cities for people.