*4.2.1 Results*

All regularity indicators are summarized in **Table 2**. It is clear from the results that the control schemes, the objective of which is to regulate the operation, dominate the regularity indicators. The coefficient of variation of headway and the level of bunching are chosen are regularity indicators. It should be noted that R-GLOSA has a minor difference from holding control since it is based on the same criterion to calculate holding time. The additional gaining comes from the speed recommendation given by the GLOSA part of the controller. Among strategies there are no significant differences in waiting time of passengers at stops. The independent application of the two DASs has no effect on system's regularity. Both have the same performance with the benchmark scenario. The regularity indicators remain unchanged regardless the TSP strength and similar to the do-nothing scenario. R-GLOSA manages to integrate the performance of holding strategy in terms of regularity and GLOSA in terms of cycle time. The cycle time with R-GLOSA is better than weak TSP and results to the least variable cycle time among all


#### **Table 2.** *Regularity performance indicators.*


**Table 3.**

*Link performance indicators.*

strategies, giving the operator the opportunity to administer more efficiently the available resources and construct a more robust schedule.

significantly reduced to the level of strong TSP. Therefore, the savings obtained in running time can compensate the additional delay at stops. The results can vary

Regularity performance indicators at line level are summarized in **Table 4**. Similarly to the results in terms of coefficient of variation per stop, R-GLODTA outperforms the other strategies with minor differences from holding and

R-GLODTA with TSP. GLOSA has a significant impact on the regularity of the line This can be explained by the fact the GLOSA adjusts the speed in order to traverse green. Acceleration and deceleration can shorten the headway between consecutive vehicles and cause platoons. Again, R-GLODTA has the lowest level of bunching between all scenarios. Passenger indicators are also recorded during simulation. As expected, differences between strategies can be observed in in-vehicle times.

**NC** 0.59 0.37 300.03 204.87 **GLODTA** 0.62 0.37 300.98 211.2 **HOLDING** 0.48 0.27 300.08 212.71 **R-GLODTA** 0.44 0.20 299.96 215.00 **R-GLODTA + TSP** 0.42 0.19 301.9 212.36 **R-GLODTA + GLOSA** 0.43 0.21 301.64 226.26 **TSP** 0.62 0.38 302.75 202.77

**CV of Headway Bunching Waiting time [s] In vehicle time [s]**

In **Figure 9**, the coefficient of variation (CV) of headway of all R-GLODTA case study scenarios is depicted. Strategies that target the mitigation of stops at traffic lights neglect the regularity of the line. Between GLODTA or TSP scenarios can be found with minor differences compared to the benchmark scenario, reporting high level of variability which propagates along the line. On the other hand, holding, All the R-GLODTA scenarios show significant improvement on maintaining the propagation of headway low. R-GLODTA outperforms holding and its performance improves further with weak TSP. Although R-GLODTA with GLOSA performs better than GLODTA and TSP, the combination is not the most effective compared

subject to the chosen holding criterion.

*Optimal Management of Electrified and Cooperative Bus Systems*

*DOI: http://dx.doi.org/10.5772/intechopen.93892*

*Coefficient of variation of headway per stop.*

to R-GLODTA and TSP.

**Figure 9.**

**Table 4.**

**113**

*Regularity performance indicators.*

The performance indicators for the links are documented in **Table 3**. It is worth noting that R-GLOSA reports the highest frequency of stops at traffic lights. However, the total average delay at traffic lights is comparable to strong TSP, which has the best performance in these two indicators. GLOSA and GLODTA perform better than holding in reducing the number of stops and the delay at traffic signals. The running time on the signalized links is also lower, meeting the objectives of both GLODTA and GLOSA. R-GLOSA reduces the running time at signalized links at the same level of weak TSP. The average speed of the vehicles increases only at the scenarios with TSP.

**Figure 8** shows the trade-off between the average delay at traffic lights and the additional time due to control. When holding is applied, the travel time increases and the additional delay at signalized intersections is not taken into account. TSP heavily prioritizes PT neglecting the impact on regularity by increasing bunching. Obviously, the application of TSP or GLOSA do not introduce any control delay at stops. GLODTA and GLOSA results to similar performance as with intermediate TSP. In contrast to TSP and GLOSA, holding is not causing any delay at traffic lights but increases significantly the additional time added due to control at stops. The delay of R-GLOSA is similar to the one holding, but delay at traffic signals is

**Figure 8.** *Tradeoff between waiting time at traffic light and holding time at stop.*

*Optimal Management of Electrified and Cooperative Bus Systems DOI: http://dx.doi.org/10.5772/intechopen.93892*

**Figure 9.** *Coefficient of variation of headway per stop.*

significantly reduced to the level of strong TSP. Therefore, the savings obtained in running time can compensate the additional delay at stops. The results can vary subject to the chosen holding criterion.

In **Figure 9**, the coefficient of variation (CV) of headway of all R-GLODTA case study scenarios is depicted. Strategies that target the mitigation of stops at traffic lights neglect the regularity of the line. Between GLODTA or TSP scenarios can be found with minor differences compared to the benchmark scenario, reporting high level of variability which propagates along the line. On the other hand, holding, All the R-GLODTA scenarios show significant improvement on maintaining the propagation of headway low. R-GLODTA outperforms holding and its performance improves further with weak TSP. Although R-GLODTA with GLOSA performs better than GLODTA and TSP, the combination is not the most effective compared to R-GLODTA and TSP.

Regularity performance indicators at line level are summarized in **Table 4**. Similarly to the results in terms of coefficient of variation per stop, R-GLODTA outperforms the other strategies with minor differences from holding and R-GLODTA with TSP. GLOSA has a significant impact on the regularity of the line This can be explained by the fact the GLOSA adjusts the speed in order to traverse green. Acceleration and deceleration can shorten the headway between consecutive vehicles and cause platoons. Again, R-GLODTA has the lowest level of bunching between all scenarios. Passenger indicators are also recorded during simulation. As expected, differences between strategies can be observed in in-vehicle times.


**Table 4.** *Regularity performance indicators.*


not account for the sequence of vehicles, undesired phenomena as formation of platoons are more likely to occur and impact the performance of a bus line.

**R-GLODTA** 61% 38% 42% 19% **R-GLODTA + TSP** 58% 37% 49% 14% **R-GLODTA + GLOSA** 62% 37% 51% 13%

*Optimal Management of Electrified and Cooperative Bus Systems*

*DOI: http://dx.doi.org/10.5772/intechopen.93892*

**Control request Controller choice**

**GLODTA Holding R-GLODTA**

This chapter has presented an integrated approach to manage electrified bus systems using Cooperative ITS. We first discussed the challenges and opportunities brought by next generation public transport systems, which require to manage the system in an integrated way. Then we introduced novel optimization methods for joint bus scheduling and charging, and real-time operational control strategies. Results in realistic simulations show how the integrated systems achieves cost

The authors acknowledge Marcin Seredynski (Volvo E-Bus Competence Center), Erika Picarelli and Andrea D'Ariano(University of Rome Tre). This project has been carried on under to the FNR-CORE Grant C16/IS/11349329 (eCoBus).

© 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,

effective, reliable and energy efficient operations.

Francesco Viti\*, Marco Rinaldi and Georgios Laskaris University of Luxembourg, Esch-sur-Alzette, Luxembourg

\*Address all correspondence to: francesco.viti@uni.lu

provided the original work is properly cited.

**5. Conclusions**

*Controller frequency.*

**Table 6.**

**Acknowledgements**

**Author details**

**115**

#### **Table 5.**

*Link performance indicators.*

The additional time added due to control actions increases the time passengers spend on board. The higher in-vehicle time can be compensated with a more robust travel time and the overall improved performance of the line.

One of the objectives of the proposed scheme is the mitigation of stop and go at signalized intersections, therefore the performance of each scenario at a link level is assessed. The results are summarized in **Table 5**.

Unquestionably, providing unconditional signal priority to PT can reduce dramatically the number of stops at signals and the corresponding delay at signalized intersections. However, this reduction will potentially penalize the rest of the traffic. R-GLODTA shows slightly increased number of stops compared to GLODTA alone. This can be explained by the fact that the combined controller prioritizes regularity over stopping at signals. Therefore, it will not exchange holding for regularity to secure passing during green. Weak TSP improves substantially the performance of R-GLODTA in terms of frequency of stops and delay at intersections. Speed adjustment with GLOSA transfers waiting time at traffic lights to running times to the links. A GLOSA advices to decelerate in order to arrive at the intersection during green, prolongs the running time between stops. All R-GLODTA scenarios result in lower total running time compared to an independent application of GLODTA or holding but higher than TSP, but they compensate with their regularity indicators, especially bunching. Among scenarios the differences of the speed are marginal.

We compare the number of TSP requests between the TSP and the R-GLODTA with TSP scenarios. The number of TSP requests is halved with R-GLODTA and with the combination of weak TSP can achieve comparable results with TSP in reducing stop and go actions at traffic lights while it contributes to the regularity of the line.

A final analysis is performed to check how many times the strategies are adopted in the simulated scenarios. **Table 6** shows the share of each control decision, i.e. when each control was needed. Fixing regularity is prioritized over reducing stops at traffic lights. Controlling actions are reduced when R-GLODTA is combined with TSP. R-GLODTA aims to address both objectives and the number of independent applications of holding or GLODTA. On the other hand, the combination with TSP or GLOSA reinforces the objective of GLODTA. The need of holding alone intensifies in these scenarios to restore regularity. With GLOSA, holding is triggered more than half of the times a controller was requested. If the changes of speed do

*Optimal Management of Electrified and Cooperative Bus Systems DOI: http://dx.doi.org/10.5772/intechopen.93892*


**Table 6.**

*Controller frequency.*

not account for the sequence of vehicles, undesired phenomena as formation of platoons are more likely to occur and impact the performance of a bus line.
