**9. Summary**

In this chapter a comprehensive literature review of transit signal priority models was presented. The review was classified into various categories including: *a)* TSP tactics, *b)* transit priority and different signal control systems including fixed-time, coordinated-actuated, fully actuated, and adaptive system, *c)* passive versus active signal priority, and conditional TSP, *d)* transit arrival time and predictive signal priority, *e)* TSP with connected vehicle, *f)* multi-modal signal priority models, and *g)* other practical consideration. There is no one-size-fits-all approach in term of applying TSP. Each transit route has different characteristics and presents various challenges which needs to be addressed differently. Each TSP treatment must be evaluated on a case by case basis. Applying a combination of different transit signal priority models that can work with the state-of-the-art technology would subtly facilitate the movement of transit and provide some performance improvement in transit operations. Most recently, the concept of Complete Streets is being introduced in emerging smart cities. The application of TSP tactics to improve the efficiency of transportation network with all users in mind will integrate well with the Complete Streets approaches. In addition, to make a transit system run faster with higher ridership, specifically busses, a strategy like transit signal priority needs to be applied together with other strategically chosen improvements. These improvements are increasing service, prioritizing transit on city streets (priority in space), redesigning bus networks, balancing bus stops, upgrading technology like fare payment system, etc. Any type of operational improvement should make sense first in order to be implemented in reality.

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**Author details**

Bahman Moghimi\* and Camille Kamga

provided the original work is properly cited.

The City College of New York, New York, NY, USA

\*Address all correspondence to: smoghim000@citymail.cuny.edu

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Transit Signal Priority in Smart Cities*

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

*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*

setback distance or decreasing signal cycle length [14, 80].

first in order to be implemented in reality.

In this chapter a comprehensive literature review of transit signal priority models was presented. The review was classified into various categories including: *a)* TSP tactics, *b)* transit priority and different signal control systems including fixed-time, coordinated-actuated, fully actuated, and adaptive system, *c)* passive versus active signal priority, and conditional TSP, *d)* transit arrival time and predictive signal priority, *e)* TSP with connected vehicle, *f)* multi-modal signal priority models, and *g)* other practical consideration. There is no one-size-fits-all approach in term of applying TSP. Each transit route has different characteristics and presents various challenges which needs to be addressed differently. Each TSP treatment must be evaluated on a case by case basis. Applying a combination of different transit signal priority models that can work with the state-of-the-art technology would subtly facilitate the movement of transit and provide some performance improvement in transit operations. Most recently, the concept of Complete Streets is being introduced in emerging smart cities. The application of TSP tactics to improve the efficiency of transportation network with all users in mind will integrate well with the Complete Streets approaches. In addition, to make a transit system run faster with higher ridership, specifically busses, a strategy like transit signal priority needs to be applied together with other strategically chosen improvements. These improvements are increasing service, prioritizing transit on city streets (priority in space), redesigning bus networks, balancing bus stops, upgrading technology like fare payment system, etc. Any type of operational improvement should make sense

without TSP and BRT [78].

**9. Summary**

effective scenario in reducing travel times (up to 26%) and delays (up to 64%), as well as increasing bus speed (up to 47%), when it was compared to scenarios

The location of bus stops along the corridor is frequently under discussion and their placement depends on the traffic demand, geometry, and policy constraints. Bus stop locations can be far-side, near-side, or mid-block. With regard to the stop setback, the near-side bus stop can change into midblock or far-side bus stop as the setback distance increases. A far-side bus stop is mostly better than a near-side bus stop [14, 79, 80]. It can either cause a very low delay or zero net-delay, whereas a near-side bus stop can reduce a delay in a few cases like reserved bus lane, but it will cause increased bus delay depending on factors like cycle length, red ratio, dwell time, and stop setback distance [79]. Cesme et al. [14] compared three main transit preferential treatments including queue jump lane, transit signal priority, and stop location evaluation over an isolated test-intersection with a fixed cycle length, and bus headway of six minutes. After extensive simulation runs under various scenarios, the results indicated that relocating the bus stop from near-side to far-side resulted in the most delay-reduction per intersection when it was compared to the two other scenarios. Results showed that a far-side bus stop was superior to the near-side one with zero setback distance. Far-side relocation's delay-saving became smaller as the dwell time at the near-side stop increased. This can be interpreted as the signal's red time and dwell time have a lot of overlap. Bus delay with near-side stops can be reduced by lowering vehicular queue interaction through increasing

**192**
