**5. Transit arrival consideration**

Predictive transit priority approaches give more flexibility to the controlling logic to enlarge the scope of signal timing to adjust itself more ahead of the transit arrival time and makes less adverse impact on the conflicting traffic. An accurate transit prediction can be a hint to passengers' departure time from point to point, so as to create more successful transfers at stops. It helps transit agencies to control and monitor their systems with more responsiveness through real time dispatching and

**189**

travel time significantly.

**6. Signal priority with connected vehicles**

Some of the advanced TSPs are based on the wireless communications using

Global Positioning System (GPS). These systems only report instantaneous

*Transit Signal Priority in Smart Cities*

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

standard deviation), and GPS data error.

scheduling, and making the transportation system a more resilient one. However, there are many parameters involved in the prediction of transit travel time, some of which are listed by [51] including: stochastic traffic flow uncertainties along the route, queue length in front of a traffic light, route length, uncertainties in dwell time (caused by the variation of passengers getting on and off at bus stops), weather conditions, times of day, statistical fluctuation in historical data (with large

Uncertainty in transit running time and dwell time is mostly pronounced and has been conducted by many scholars who have been developing predictive transit arrival models. Bus dwell time itself consists of passenger boarding and alighting time, door opening and closing time, and clearance time. Hence, predicting the dwell time is cumbersome and a good prediction of transit dwell time, specifically when there is a nearside bus stop, increases the precision of transit arrival time at the target intersection. One of the primary studies about dwell time prediction at nearside bus stops was presented by Kim and Rilett [52]. They used a regression model to come up with an upper and lower bound for dwell time with respect to the bus load, headway, and schedule adherence. Such prediction was included in the improved TSP algorithm which then was applied over a fixed-time signal control. It benefited the operation of bus systems well. Lee et al. [15] developed a predictive model for dwell time at a nearside bus stop, based on headway and passenger arrival rate. Ekeila et al. [53] presented a linear regression and applied empirical Bayesian and Kalman filtering refinement to improve the prediction performance. The developed model, applied to LRT's arrival time (including running time and dwell time) to predict its boundary length, was one standard deviation from its arrival time. Some researchers have attempted to predict the transit arrival time further ahead of the target signalized intersection. Their focuses were mostly on the prediction of the transit travel time (running time) together with the dwell time consideration. Zlatokovic et al. [54] developed predictive priority for LRT in Salt Lake City, Utah which could reduce train travel time by 20–30%. The logic used almost all TSP tactics along with peer-to-peer communication between intersections. With the peer-to-peer communications, the logic activated priority when the train was stopped at the adjacent intersection, pointing to the target intersection to be prepared for the arriving train. Wadjas and Furth [55] used advanced detectors in order to detect the light-rail's arrival more ahead of the target intersection. Once the transit is detected, the logic applies the cycle length adaptation which lengthens or shortens the cycles/phases in such a way as to find the best match aligned with the TSP tactics. The logic applied to the signals with the fully actuated control. They found that the logic functioned better in enhancing transit travel time and regularity compared to the simple preemption, with a negligible impact on general vehicular traffic and pedestrians. Moghimi et al. [18] developed a model that calculates the expected remaining dwell time, added to the adaptive bus running time estimation, at each time-step of the simulation in order to predict the bus arrival time. Their presented model reduced bus net delay to less than 5 seconds per intersection. Moghimi et al. [56] also developed a look-ahead TSP to include a longer range of prediction in order to provide transit priority far in advanced. Their presented model outperformed the conventional TSP model and improved the reliability of

#### *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*

vehicles, but rather on applying priority logic based on some conditions.

Unconditional priority means granting TSP tactics to the upcoming transit vehicles regardless of cross-street traffic or queue length, state of signal, or transit arrival time. It is more of an aggressive approach toward granting priority. In other words, unconditional priority is beneficial in improving bus delays, travel time, and reliability when the bus frequency is low, and when the traffic demand over signal is low. On the other hand, conditional priority grants transit signal priority only if the state of signal and bus arrival meet some defined requirements. For instance, conditional TSP can be applied if some of the following criteria are met: transit is behind schedule (e.g. let us say 5 min behind as being late), transit passenger-occupancy is more than a defined threshold, the intersection is under saturated level, no queue spillback is happening, the signal did not have a priority request in the previous cycle, etc. It is more complicated than the unconditional priority because it needs more updated information about transit and intersections. Conditional priority will improve bus headway irregularity, crowding, and mean running time to almost the same levels as what absolute TSP. More importantly, conditional TSP makes transit running time less varied (less standard deviation of running time), which indeed improves the reliability of transit scheduling service. The performance of conditional TSP was studied and found that it is more effective for bus routes experiencing more severe lateness [47–49]. Meanwhile, person-based signal priority approach has been recently introduced optimizes signals and applies conditional transit priority based on transit and vehicle passenger-occupancy conditions [50].

Predictive transit priority approaches give more flexibility to the controlling logic to enlarge the scope of signal timing to adjust itself more ahead of the transit arrival time and makes less adverse impact on the conflicting traffic. An accurate transit prediction can be a hint to passengers' departure time from point to point, so as to create more successful transfers at stops. It helps transit agencies to control and monitor their systems with more responsiveness through real time dispatching and

vehicle passes the check-in detector, which is located upstream of the signal. Where to put the check-in detector is not deterministic and its optimal location is mostly related to traffic demand, and signal timing. The result demonstrated that putting a detector between 450 ft. (150 m) and 900 ft. (300 m) upstream of the intersection can output better results [43]. Meanwhile, the detection should cancel out the priority request when transit passes the stop-line detectors (check-out detectors). Those are located just after the traffic light, indicating the transit vehicle received priority, could pass through, and it is the time to start compensating the amount of time taken from the conflicting phases. Active TSP has been demonstrated as a better approach to improve transit performance, to better accommodate uncertain arrival time, and make on-street transit more reliable, faster, and cost-effective [42, 44]. Active TSP has been taken into consideration worldwide. For instance, applying active signal priority was studied on the two old and large street-car systems in Melbourne, Australia, and Toronto, Canada [45]. The results confirmed that such an approach is a cost-effective approach to manage traffic systems. Song et al. [46] compared the GPS-based TSP and traditional TSP on two corridors in Utah, and it was found that GPS-based TSP reduced the same delay and travel time similarly to the traditional TSP. Surprisingly, the GPS-based signal priority system was effective in the flexible detection zone and could bring conditional priority into its logic while causing smaller impact on the side-street traffic. Active priority has recently focused its attention not only on the presence of transit

**188**

**5. Transit arrival consideration**

scheduling, and making the transportation system a more resilient one. However, there are many parameters involved in the prediction of transit travel time, some of which are listed by [51] including: stochastic traffic flow uncertainties along the route, queue length in front of a traffic light, route length, uncertainties in dwell time (caused by the variation of passengers getting on and off at bus stops), weather conditions, times of day, statistical fluctuation in historical data (with large standard deviation), and GPS data error.

Uncertainty in transit running time and dwell time is mostly pronounced and has been conducted by many scholars who have been developing predictive transit arrival models. Bus dwell time itself consists of passenger boarding and alighting time, door opening and closing time, and clearance time. Hence, predicting the dwell time is cumbersome and a good prediction of transit dwell time, specifically when there is a nearside bus stop, increases the precision of transit arrival time at the target intersection. One of the primary studies about dwell time prediction at nearside bus stops was presented by Kim and Rilett [52]. They used a regression model to come up with an upper and lower bound for dwell time with respect to the bus load, headway, and schedule adherence. Such prediction was included in the improved TSP algorithm which then was applied over a fixed-time signal control. It benefited the operation of bus systems well. Lee et al. [15] developed a predictive model for dwell time at a nearside bus stop, based on headway and passenger arrival rate. Ekeila et al. [53] presented a linear regression and applied empirical Bayesian and Kalman filtering refinement to improve the prediction performance. The developed model, applied to LRT's arrival time (including running time and dwell time) to predict its boundary length, was one standard deviation from its arrival time.

Some researchers have attempted to predict the transit arrival time further ahead of the target signalized intersection. Their focuses were mostly on the prediction of the transit travel time (running time) together with the dwell time consideration. Zlatokovic et al. [54] developed predictive priority for LRT in Salt Lake City, Utah which could reduce train travel time by 20–30%. The logic used almost all TSP tactics along with peer-to-peer communication between intersections. With the peer-to-peer communications, the logic activated priority when the train was stopped at the adjacent intersection, pointing to the target intersection to be prepared for the arriving train. Wadjas and Furth [55] used advanced detectors in order to detect the light-rail's arrival more ahead of the target intersection. Once the transit is detected, the logic applies the cycle length adaptation which lengthens or shortens the cycles/phases in such a way as to find the best match aligned with the TSP tactics. The logic applied to the signals with the fully actuated control. They found that the logic functioned better in enhancing transit travel time and regularity compared to the simple preemption, with a negligible impact on general vehicular traffic and pedestrians. Moghimi et al. [18] developed a model that calculates the expected remaining dwell time, added to the adaptive bus running time estimation, at each time-step of the simulation in order to predict the bus arrival time. Their presented model reduced bus net delay to less than 5 seconds per intersection. Moghimi et al. [56] also developed a look-ahead TSP to include a longer range of prediction in order to provide transit priority far in advanced. Their presented model outperformed the conventional TSP model and improved the reliability of travel time significantly.
