**7. Multimodal traffic signal**

In transportation systems, there are many users at signalized intersections that consist of passenger cars, commercial vehicles, busses, streetcars, emergency vehicles, snowplows, bikes, and pedestrians. The idea of providing TSP is not just to prioritize public transit but making other modes of transportation remain relevant in any developed TSP system. The conventional traffic controlling system treats all vehicles of different class/mode as an aggregate flow of traffic into a signal flow. This approach does not adequately consider each mode based on weight per se and is not well-aligned with the system operating objective [60]. On the other hand, treating each mode separately would result in a sub-optimal system performance [61]. The better manner of treatment is to come up with an algorithm that can consider all traffic modes based on their weight in order to reach the overall objective function. Recently, many new researchers have been developing algorithms that

**191**

*Transit Signal Priority in Smart Cities*

TSP algorithm more holistic.

*DOI: http://dx.doi.org/10.5772/intechopen.94742*

**8. Other practical considerations**

can better quantify such objective function including all modes and then make the

He et al. [62] formulated a mixed integer linear program (MILP) for robust multiple priority requests with different modes including busses, pedestrians, and cars. The mixed-integer nonlinear program was used by Christofa and Skabardonis [63] to minimize total person delays, while assigning priority to transit based on the passenger occupancy. It is true that at one signalized intersection, pedestrian is a dominant mode, at another one, bus or truck is a dominant mode. Hence, a better approach would be to make the signals friendly with respect to the relative importance of each signal. Zamanipour et al. [64] developed a new approach that capture a relative importance of each section of the traffic signal and then establish a priority policy for that. They enhanced the work done by [62] and used such a flexible implementation algorithm that considers real time actuation on top of the MILP [60]. The developed model was designed to be utilized under a connected vehicle environment and was tested in San Mateo, California and Anthem, Arizona to confirm the better performance of the multimodal control over the fully actuated control. Later, Beak et al. [65] improved the MILP model to consider peer-to-peer signal priority control in a corridor. They designed a signal priority control framework to address the limitation of the effective range of DSRC and the extent of the intersection map message. In today's complex transit network, multiple priority requests at intersections occur frequently. Although applying a first-come-first-serve policy is a widely acceptable approach in many cities, it cannot be the best option and sometimes can perform worse compared to providing no priority [66]. Therefore, some scholars tried to challenge this approach and take better advantage of priority logic to make it more beneficial in terms of minimizing total transit delay. Head et al. [67] developed a mixed integer programming for multiple-priority requests problems based on a precedence graph. Their findings demonstrated that their model performed better in minimizing total priority delay compared to the first-come-first-serve policy. He et al. [68] developed a fast heuristic algorithm to provide priority to simultaneous multiple requests in real time using V2I communication. Ma and Bai [69] proposed a decision tree for optimizing TSP-requests sequence. Ma et al. [70] used a dynamic programming model to generate an optimal sequence for the conflicting requests.

Another approach being mentioned in the literature relates to providing facilities with exclusive or dedicated bus lanes and giving transits/busses exclusive right of way. The advantage of dedicated/dedicated bus lanes is to free busses from traffic interference and benefit transit operation. However, taking a lane from general traffic and assigning it to transit may increase general traffic travel time, specifically when congestion is high [71, 72]. Eicher and Daganzo [73] proposed a bus lane with intermittent priority which allows general traffic to use the dedicated bus lane dynamically when it is not used by busses. Their idea is applicable on bus route with low frequencies. Indeed, TSP associated with exclusive bus lane [13, 74] and TSP with intermittent priority [75] revealed improvement in bus travel time and its reliability. An example of a transit system with dedicated lane is bus rapid transit (BRT), which is an integrated system of facilities that plays a significant role in today's urban transportation and can reasonably improve bus speed, travel time, reliability, as well as serving as a catalyst for redevelopment [76]. TSP with BRT can produce significant enhancement, because in such a system, TSP can be applied to any time without being worried about queue length ahead of the transit [77]. In addition, simulation results demonstrate that applying TSP with BRT was the most

#### *Transit Signal Priority in Smart Cities DOI: http://dx.doi.org/10.5772/intechopen.94742*

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

real time which makes dwell time prediction more accurate.

can provide better traffic resolution.

vehicle location data. Also, with the advances in emerging technologies, vehicles can communicate with each other (V2V) and with the infrastructure (V2I), through 5.9 GHz dedicated short-range communication (DSRC). Using this technology, each vehicle is equipped with an on-board unit (OBU) that broadcasts the vehicle speed, and acceleration at 10 times a second. A road side unit (RSU) is installed at the intersection to broadcast traffic signal status and also intersection geometry maps. The RSU can receive messages from surrounding vehicles and

As soon as a transit vehicle enters the Dedicated Short-Range Communication (DSRC) range of intersection, it receives the map of the intersection and determines its location. Then, it broadcasts a request message and asks for priority. The RSU at the intersection receives the request and provides treatment. If the vehicle's speed changes dramatically, (e.g. the vehicle joins a queue) or if the transit vehicle stops at the bus stop, an updated request is broadcasted. The updated request is received by RSU and proper actions are planned. Connected vehicle technology can provide countable data because it updates vehicle dynamical traffic-related information like speed, acceleration, location, and other vehicle data in real time [57]. Such technology also provides the information about passenger counts (sitting/ standing on transit) and at stops which can be transmitted to the signal controller in

Hu et al. [5] used TSP with connected vehicles (TSP-CV) technology and compared their logic with conventional TSP and no-TSP scenarios. Results reported that the proposed logic reduced bus delay between 9–84% as compared to conventional TSP, as well as outperforming the no-TSP scenario. Meanwhile, it was shown that as volume-to-capacity ration increases (approaching to v/c equal to 1), the difference between TSP using connected vehicle and conventional TSP decreases. Hu et al. [58] continued their studies on conditional TSP-CV and proposed a person-based optimization method along with recommending a desired speed to the bus. The conditional logic was applied only to busses that are behind schedule and it was tested on two closely spaced intersections with fixed-time control. The results revealed that conditional TSP-CV performed better, specifically when the v/c is under saturated, and again it was found that as demand increases, when it approaches capacity, the benefit of TSP decreases. Lee et al. [59] tested the application of TSP in connected vehicle technology over a smart road test bed in Blacksburg, Virginia. The experiment results confirmed that the TSP-CV logic provided bus green extension with a 100% success rate, together with reducing bus delay between 32 to 75% as com-

In transportation systems, there are many users at signalized intersections that consist of passenger cars, commercial vehicles, busses, streetcars, emergency vehicles, snowplows, bikes, and pedestrians. The idea of providing TSP is not just to prioritize public transit but making other modes of transportation remain relevant in any developed TSP system. The conventional traffic controlling system treats all vehicles of different class/mode as an aggregate flow of traffic into a signal flow. This approach does not adequately consider each mode based on weight per se and is not well-aligned with the system operating objective [60]. On the other hand, treating each mode separately would result in a sub-optimal system performance [61]. The better manner of treatment is to come up with an algorithm that can consider all traffic modes based on their weight in order to reach the overall objective function. Recently, many new researchers have been developing algorithms that

**190**

pared to No TSP scenario.

**7. Multimodal traffic signal**

can better quantify such objective function including all modes and then make the TSP algorithm more holistic.

He et al. [62] formulated a mixed integer linear program (MILP) for robust multiple priority requests with different modes including busses, pedestrians, and cars. The mixed-integer nonlinear program was used by Christofa and Skabardonis [63] to minimize total person delays, while assigning priority to transit based on the passenger occupancy. It is true that at one signalized intersection, pedestrian is a dominant mode, at another one, bus or truck is a dominant mode. Hence, a better approach would be to make the signals friendly with respect to the relative importance of each signal. Zamanipour et al. [64] developed a new approach that capture a relative importance of each section of the traffic signal and then establish a priority policy for that. They enhanced the work done by [62] and used such a flexible implementation algorithm that considers real time actuation on top of the MILP [60]. The developed model was designed to be utilized under a connected vehicle environment and was tested in San Mateo, California and Anthem, Arizona to confirm the better performance of the multimodal control over the fully actuated control. Later, Beak et al. [65] improved the MILP model to consider peer-to-peer signal priority control in a corridor. They designed a signal priority control framework to address the limitation of the effective range of DSRC and the extent of the intersection map message.

In today's complex transit network, multiple priority requests at intersections occur frequently. Although applying a first-come-first-serve policy is a widely acceptable approach in many cities, it cannot be the best option and sometimes can perform worse compared to providing no priority [66]. Therefore, some scholars tried to challenge this approach and take better advantage of priority logic to make it more beneficial in terms of minimizing total transit delay. Head et al. [67] developed a mixed integer programming for multiple-priority requests problems based on a precedence graph. Their findings demonstrated that their model performed better in minimizing total priority delay compared to the first-come-first-serve policy. He et al. [68] developed a fast heuristic algorithm to provide priority to simultaneous multiple requests in real time using V2I communication. Ma and Bai [69] proposed a decision tree for optimizing TSP-requests sequence. Ma et al. [70] used a dynamic programming model to generate an optimal sequence for the conflicting requests.
