Traffic Light Signal Timing Optimization

Signalized intersections often act as recurring bottlenecks in urban street networks. There is a significant opportunity to improve traffic operations and safety at signalized intersections with new information that has become available by recent advancements in sensor technology and connected vehicles. Having said that, optimal signal control is a very challenging problem from a mathematical perspective. There is a need to develop methodologies that create a balance between computational efficiency and accuracy as signal control decisions need to be found in real-time and need to improve traffic operations.

Distributed Traffic Signal Control in Urban Street Networks

We have developed distributed optimization and coordination techniques to find high-quality signal timing parameters in real-time. The main idea is to decompose a large-scale transportation network spatially to intersection-level subproblems, optimize signal timing parameters in each intersection utilizing its own computational power, and coordinate the decisions among intersections using infrastructure-to-infrastructure communications. The distribution significantly reduces the computational complexity and allows finding solutions in real-time while the coordination pushes the solutions towards system-level optimality. Our signal timing methodologies are proactive: they predict traffic state in the future and optimize signal timing parameters based on these predictions. Our simulation results verify that significant achievement in traffic operations are possible while signal timing parameters are optimized in real-time.

Here are some of our publications on this topic:

  1. Mehrabipour M.* and A. Hajbabaie, 2017. A Cell Based Distributed-Coordinated Approach for Network Level Signal Timing Optimization. Computer-Aided Civil and Infrastructural Engineering, Vol. 32.8, pp 599-616.
  2. Islam S.* and A. Hajbabaie, 2017. Distributed – Coordinated Signal Timing Optimization in Connected Transportation Networks. Transportation Research Part C: Emerging Technologies, Vol. 80, pp 272-285.

Distributed Signal Control with a Mixed-fleet of Connected and Unconnected Vehicles

Connected vehicles are equipped with wireless technology and can share their location, speed, and acceleration rate (among other data) with each other and with traffic signal controllers. Therefore, traffic signal controllers will have a lot more information to optimize the timing of traffic lights. We utilized this capability in our methodologies to improve traffic state observability and consequently improve traffic operations by optimizing signal timing parameters. We have developed methodologies that fuse loop detector and connected vehicle data to predict the location of unconnected vehicles over time. Therefore, signal controllers have a more accurate estimation of the number of upcoming vehicles, their spatial distribution, and estimated arrival times. These data are used by the controller to improve traffic operations. Our simulation results show that even with 10% connected vehicles in the traffic stream, the number of completed trips can be increased by more than 3%.

Here are some of our publications on this topic:

  1. Islam S.*, M. Tajalli*, R. Mohebifard*, and A. Hajbabaie, 2021. The Effects of Connectivity and Traffic Observability on Adaptive Traffic Signal Control. Transportation Research Record, Submitted.
  2. Islam S.*, A. Hajbabaie, and H. Aziz, 2020. A Real-Time Network-Level Traffic Signal Control Methodology with Partial Vehicle Information. Transportation Research Part C: Emerging Technologies, Vol 121, 102830.
  3. Tajalli M.*, M. Mehrabipour*, and A. Hajbabaie, 2020. Cooperative Signal Timing and Speed Optimization in Connected Urban-Street Networks. IEEE Transactions on Intelligent Transportation Systems, In Press.
  4. Mohebifard R.*, S. Islam*, and A. Hajbabaie, 2019. Cooperative traffic signal and perimeter control in semi-connected urban-street networks. Transportation Research Part C: Emerging Technologies, Vol. 104, pp 408-427.

An Enhanced Cell Transmission Model for Multi-class Signal Timing

A common way to facilitate the movement of transit vehicles at signalized intersections is to grant them a priority over passenger cars. Existing transit signal priority-based methods accommodate transit vehicles on a movement by either extending the associated green time or reducing the duration of the red signal on other movements. However, research shows that the existing methods become less efficient when several transit vehicles arrive at an intersection on conflicting movements that compete for the green time. The cell transmission model (CTM) can explicitly consider different vehicle classes; however, it faces to issues: the first is that transit vehicles may progress from one cell to the next or from one link to the next without following passenger-cars in front of them; the second possible issue is that a bus may occupy many cells at a time step and never exit a cell completely. We presented a methodology for multi-class signal control based on the CTM network loading concept that prevents transit vehicles from jumping over passenger cars and processes them as integer entities. Our simulation studies show that 0% CV penetration rate, the average bus delay was reduced by 1% to 70% and car delay by 52% to 76% compared to existing transit signal priority approaches. Here is our most recent paper on this topic:

  1. Islam S.* and A. Hajbabaie, 2021. An Enhanced Cell Transmission Model for Multi-class Signal Control. IEEE Transactions on Intelligent Transportation Systems, Submitted.

Fundamentals of Network-Level Traffic Signal Control

We consistently research the fundamental issues of network-level signal control. Traffic signal timing optimization is a complex mathematical program (NP-complete). We work on developing optimization programs that provide a balance between the quality of solutions and efficiency. Our work covers topics such as selecting the most appropriate objective function; integrating signal timing with traffic assignment, traffic metering, and speed harmonization; developing optimization techniques to find the optimal solution to the problem, and including energy consumption in the signal timing optimization. Here are some of our publications on this topic:

  1. Tajalli M.*, M. Mehrabipour*, and A. Hajbabaie, 2020. Cooperative Signal Timing and Speed Optimization in Connected Urban-Street Networks. IEEE Transactions on Intelligent Transportation Systems, In Press.
  2. Islam S.*, and H. Aziz. A. Hajbabaie, 2020. Stochastic Gradient-based Optimal Signal Control with Energy Consumption Bounds. IEEE Transactions on Intelligent Transportation Systems, In Press.
  3. Mohebifard R.*, S. Islam*, and A. Hajbabaie, 2019. Cooperative traffic signal and perimeter control in semi-connected urban-street networks. Transportation Research Part C: Emerging Technologies, Vol. 104, pp 408-427.
  4. Mohebifard R.* and A. Hajbabaie, 2019. Optimal Network-level Traffic Signal Control: A Benders Decomposition-based Solution Algorithm. Transportation Research Part B: Methodological, Vol. 121, pp 252-274.
  5. Hajbabaie A. and R. F. Benekohal, 2015. A Program for Simultaneous Network Signal Timing Optimization and Traffic Assignment. IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 5, pp 2573-2586.
  6. Hajbabaie A. and R. F. Benekohal, 2013. Traffic Signal Timing Optimization: Selecting the Objective Function. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2355, pp 10-19.