IMPROVE AMBULANCE RESPONSE TIME THROUGH CONNECTED AUTONOMOUS VEHICLES

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IMPROVE AMBULANCE RESPONSE TIME THROUGH CONNECTED AUTONOMOUS VEHICLES

A Simulation Study in Cooperation with UMCG Ambulancezorg

Master thesis, Msc, Supply Chain Management

University of Groningen, Faculty of Economics and Business

28 January 2019

Reind Jaap Hoekman S3273849

r.j.hoekman@student.rug.nl

Supervisor University of Groningen Dr. E. Ursavas

Second supervisor University of Groningen Dr. X. Zhu

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2 ABSTRACT

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3 TABLE OF CONTENTS ABSTRACT ... 2 PREFACE ... 5 1. INTRODUCTION ... 6 2. THEORETICAL BACKGROUND ... 8

2.1 Connected Autonomous Vehicles (CAV) ... 8

2.2 CAV Intersections ... 9

2.3 Vehicles-Intersection Coordination Scheme (VICS) ... 10

2.4 Control strategies ... 11

3. METHODOLOGY ... 13

3.1 Simulation model ... 13

3.2 Case study ... 14

3.3 Vehicle arrival generation ... 15

3.4 Ambulance preemption ... 15

3.5 Data collection ... 15

4. SIMULATION ... 16

4.1 Performance Indicators ... 16

4.2 Emergency vehicle detection distance ... 17

4.3 Simulation of VICS and FTC ... 18

4.3.1 Simulation of fixed time control (FTC) ... 18

4.3.2 Simulation of Vehicle-intersection coordination scheme (VICS) ... 19

4.4 Preemption allocation determination ... 21

4.4.1 Determination of discharged vehicles ... 21

4.4.2 Velocity predecessor ... 22

4.4.3 Vehicle velocity ... 24

5. DISCUSSION ... 25

5.1 Intersection operation without EV arrival ... 25

5.2 VICS efficiency at the intersection... 25

5.3 Application of priority ... 27

5.4 VICS safety and response time ... 28

5.5 Limitations of research ... 29

6. CONCLUSION ... 29

6.1 Considerations to prepare for VICS ... 29

6.2 Future research ... 30

7. REFERENCES ... 32

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5 PREFACE

This thesis is the final step to completion of the master Supply Chain Management at the University of Groningen. The subject of the thesis is in line with my passion for digital innovation and gave me the opportunity to look into the future of smart passenger vehicles and the benefits that will be brought along. This paper is the result of hard work over the past semester. I would like to thank my first supervisor Dr. E. Ursavas for the help and feedback during the research and writing of the thesis. Secondly, I would like to thank Ir. J. Hatenboer for providing the opportunity to write the thesis for the UMCG Ambulancezorg and the enthusiasm he provided during all meetings. Further, I would like to thank W. Ijedema from the municipality of Groningen for providing the requested vehicle data. Finally, I like to express my profound gratitude to my friends and family for their support during this thesis project.

Groningen, January 28 2018

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1. INTRODUCTION

The survival rate and recovery process of patients significantly improves with immediate response to injuries (Aringhieri, Bruni, Khodaparasti, & van Essen, 2017). Because time is critical to patient survival, the response time of an ambulance should be as low as possible (Lam et al., 2015). To achieve the lowest possible response time, ambulances have priority in case of an emergency. Despite this priority, accidents with other road users still occur. A study done by the Dutch Instituut Fysieke Veiligheid (2017) showed that between 2010 and 2015 38% of the ambulance accidents in the Netherlands occurred at an intersection while vehicles from other approaches were given a green signal. Besides the risk of accidents, crossing an intersection will cause a delay in the ambulance response time due to congestion and lower operating speed as permitted by the Dutch government (Lam et al., 2015; Qin & Khan, 2012). Traffic congestion at the arriving lanes arises when the efficiency of an intersection is too low to process all vehicles (Nellore & Hancke, 2016). Therefore it can be concluded that safe, fast and efficient crossing of an intersection for ambulances is of high importance (Zaffar, Rajagopalan, Saydam, Mayorga, & Sharer, 2016).

Various measures are in place to reduce response time, for example optimizing the location and allocation of ambulances and improving control strategies for traffic light allocation (Aboueljinane, Sahin, & Jemai, 2013; Lam et al., 2015; McCormack & Coates, 2015; Younes & Boukerche, 2017; Zaffar et al., 2016). Recent studies even show interest in using connected vehicles for improving traffic signal preemption for emergency vehicles (Noori, Fu, Shiravi, & Board, 2016; Qin & Khan, 2012). In extension to the interest in connected vehicle traffic control strategies, concepts of intersections coordinated for connected autonomous vehicles (CAV) gradually achieve more academic attention. Collectively, these studies show the possibility of eliminating collisions, reducing delay and reduce travel time at intersections (Dresner & Stone, 2008; Ilgin Guler, Menendez, & Meier, 2014; Kamal, Imura, Hayakawa, Ohata, & Aihara, 2015; Lee & Park, 2012; Lee, Park, Malakorn, & So, 2013; Lu & Kim, 2016; Zheng et al., 2017).

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(VICS) designed by Kamal et al. (2015). The VICS operates with connected autonomous vehicles (CAV) and ensures safe and efficient crossing of an intersection without using traffic lights (Kamal et al., 2015; Weiß, 2011). A presumption to the VICS is full adoption of CAVs. This paper aims to reduce response time for ambulances without compromising intersection efficiency and safety of ambulances.

Building upon the proposed control strategy of Qin and Khan (2012) and the insights provided by Kamal et al. (2015) an adapted control strategy is presented. The control strategies designed by Qin and Khan (2012) offer traffic signal timing for EV preemption. EV preemption strategy for the VICS will be adapted from the real-time control strategy of Qin and Khan (2012). The insights and findings of Kamal et al. (2015) provide values that are applied as parameters in the adapted control strategy. The new adapted strategy, imitating the VICS, will be compared to the fixed-time control strategy of Qin and Khan (2012) given the traffic intensities of the municipality of Groningen. The research seeks to address the following question,

How will a preemption strategy in a vehicle-intersection coordination scheme affect ambulance response time without compromising on safety and intersection efficiency?

This research will contribute to literature by adapting the real-time control strategy for emergency vehicle preemption to a VICS with Dutch traffic characteristics. It is analyzed from the simulation whether the adapted control strategy will ensure a more efficient operation for emergency vehicles and the intersection. Managerial relevance is found in providing insight to the UMCG Ambulancezorg on safety and efficiency at the intersection in a VICS. These insights can be useful to anticipate on future changes towards a CAV environment.

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2. THEORETICAL BACKGROUND

This chapter provides an informative background of the concepts used in this research. The first section provides insight in the functioning of CAVs and the consequences they have on traffic. In addition, a review of the related control systems of CAV coordinated intersections is presented. Section three provides an explanation of the adopted VICS designed by Kamal et al. (2015). At last, it is explained how the VICS will be linked to the control strategies of Qin and Khan (2012). A conceptualized representation of the research is visualized in the in the research framework presented at the end of this chapter.

2.1 Connected Autonomous Vehicles (CAV)

According to Vasirani & Ossowski (2012) a CAV operates by software on behalf of the human owner and must be able to perform every task in traffic a human driver performs as well. In addition to the basic performance, a CAV will operate with higher accuracy, is able to monitor the entire surrounding and react instantly to changes (Dresner & Stone, 2008).

Communicative technologies as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and device (V2D) communication, collectively referred to as vehicle-to-everything (V2X) are a necessity for CAVs to function and enables beyond-line-of-sight communication (Bazzi, Zanella, & Masini, 2016; Ge et al., 2018; Jin, Ma, & Kosonen, 2017). With the technological eruption of V2X, vehicles can now communicate mutually to transmit real-time data to the infrastructure (Goodall, Smith, & Park, 2013; He, Head, & Ding, 2014; Xiang & Chen, 2016). To ensure robust and reliable wireless communication for CAVs, resources must efficiently be allocated by use of Dedicated Short-Range Communications, Wi-Fi and 5G technology (Xie, Gartner, & Chowdhury, 2017).

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accidents (Kamal et al., 2015). CACC can also be used to provide a safe and clear pathway on the highway to the ambulance. In case of emergency CACC can force vehicles to leave the lane and merge with traffic in adjacent lanes (Jordan, Cetin, & Robinson, 2013; Rios-Torres & Malikopoulos, 2017). However, instead of providing preemption at the highway, this paper solely focusses on providing preemption at intersections.

CAVs able to cross intersections without traffic lights and adjust to demands of ambulances are the most advanced autonomous vehicles. These CAVs are level 4 and 5 of automation as defined by the Society of Automotive Engineers (SAE) (Favarò, Eurich, & Nader, 2018; Hobert et al., 2016). Availability of SAE level 5 CAVs are expected until 2025 at earliest, while full penetration will have to wait until 2050. Although this takes time, studies suggest there is no threshold to the effectiveness of partial penetration of CAVs in terms of highway capacity (Conceicao, Ferreira, & Steenkiste, 2013; Olia, Razavi, Abdulhai, & Abdelgawad, 2018).

2.2 CAV Intersections

A CAV intersection characterizes itself by the substitution of traffic lights by algorithms to coordinate the crossing vehicles (Dresner & Stone, 2008; Kamal et al., 2015; Lee & Park, 2012; Zheng et al., 2017). CAV intersections rely on full penetration of CAVs and are designed to solve inconveniences that are common to current traffic such as collisions, waiting time and delay (Zheng et al., 2017). This will improve traffic flow and reduce congestions resulting in lower response times (García-Nieto, Alba, & Carolina Olivera, 2012; Jin, Ma, & Kosonen, 2017; Talking Traffic, 2018).

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cooperative adaptive cruise control, referred to as iCACC. Similar to the research of Lioris et al. (2017) Zohdy and Rakh (2016) suggest a solution where a request is received from a platoon as a whole to pass the intersection and the iCACC will advise the platoon on the optimal course. However, in contrast to Kamal et al. (2015), their approaches fail to manage individual vehicles in a holistic approach that forces complete platoons to accelerate, stop or decelerate and thereby reducing overall intersection efficiency. Finally, Zheng et al. (2017) designed a Cooperative Vehicle Control (CVC) algorithm showing strong similarities to the VICS proposed by Kamal et al. (2015) in terms of design and results of the simulation. Despite their extensive results, their architecture needs to be improved to prevent vehicles from stopping at the intersection at all scenarios (Zheng et al., 2017). Next, the vehicles in the VICS of Kamal et al. (2015) operate more smoothly than the vehicles in the CVC of Zheng et al. (2017) due to the stricter limitations in acceleration and deceleration.

2.3 Vehicles-Intersection Coordination Scheme (VICS)

The vehicle-intersection coordination scheme is a CAV intersection as explained in the previous section, designed by Kamal et al. (2015) that ensures an idling free, smooth flow for all approaching vehicles from all lanes at the same time.

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11 2.4 Control strategies

EV preemption strategies ensure that an EV will cross the intersection safe at operating speed. The control strategies designed by Qin and Khan (2012) are designed to take over traffic control at signalized intersections by an EV preemption strategy in case of an approaching EV. The fixed time control strategy (FTC) and the real-time control strategy detect an approaching EV and allocate green time to the approach to ensure that the EV can cross the intersection at operating speed safely (Qin & Khan, 2012).

The fixed-time control strategy as applied in the article of Qin and Khan (2012), switches from normal operation of traffic signals at an intersection to an EV signal preemption (EVSP) when an EV is detected. In the normal operation of the intersection, green time is allocated to the approaches of the intersection in two phases. In the first phase green time is allocated to the main road, followed up by yellow time and red time. If phase one is completed, phase two allocates green time to the side roads while the main road has a red signal. These two phases are repeated infinitely. If an EV is detected, a fixed number of green time will be allocated to the approach from which the EV arrives. Allocation of a fixed number of green time disrupts the signal phases and allocates red signal time to the conflicting approaches. The real-time control strategy, proposed by Qin and Khan (2012) dynamically allocates green time to the approaches, based on V2V communication to reduce response time and minimize impact on other traffic at the intersection.

The real-time control strategy as developed by Qin and Khan (2012) will be adapted in this research to operate similar to the VICS of Kamal et al. (2015) and allow an EV preemption strategy in the VICS. Qin and Khan (2012) developed software to simulate their strategies in MATLAB. The analysis of the simulated strategies illustrates the efficiency of the EVSP by the number of failures for the control strategy, the average green time for the side road, average spare time and at last the average queue lengths in vehicles per approach. Elaborating upon the simulation model of Qin and Khan et al. (2015) a case study will be performed, based on the historical data of the traffic characteristics of the municipality of Groningen.

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and at the average queue length in vehicles per approach. At last, the operational effectiveness of the preemption strategy for the VICS is evaluated. Since vehicles from side roads will decelerate to minimize the risk of a collision with the EV, the EV will cross the intersection safely at operating speed. Vehicles preceding to the EV may have a lower speed and become an obstruction to the EV. Therefore, it is studied if preceding vehicles need priority to become no obstruction to the EV.

By adapting the control strategy of Qin and Khan (2012) to the VICS of Kamal et al. (2015) insights concerning applicability of VICS at Sontplein are yielded by performing a simulation. The outcome of the simulation should contribute to the insights of the UMCG Ambulancezorg to prepare for adoption of CAVs and provide academic evaluation of the VICS of Kamal et al. (2015). The research framework is presented in Figure 2.1. The simulation of the control strategies will be explained in chapter 3.

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3. METHODOLOGY

The purpose of this chapter is to explain how the article of Qin and Khan (2012) and Kamal et al. (2015) contribute to the simulation of the of a preemption strategy in the VICS. The first section explains how the VICS is developed based on the control strategies of Qin and Khan (2012). In the second section, a case study provided by the UMCG Ambulancezorg is presented. Section three provides the first step of the simulation which generates vehicle arrival. The fourth section examines how the preemption strategy has to be incorporated in the VICS of Kamal et al. (2015). The last section gives an overview of historical data on the traffic characteristics of the municipality of Groningen.

3.1 Simulation model

The methodological approach taken in this study is inspired by the ones proposed by Qin and Khan (2012) and Kamal et al. (2015). The VICS as simulated in this research builds upon the design of the real-time control strategy of Qin and Khan (2012). The real-time control strategy is adapted to replace the current traffic signal operation by the traffic operation of the VICS of Kamal et al. (2015) without traffic signals.

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EV preemption is simulated by use of the adapted control strategy. However, for the actual VICS design of Kamal et al. (2015) a constraint should be added to the constrained nonlinear optimization problem to organize the preemption strategy. The constraint should guarantee that other vehicles than the EV adjust their velocities and acceleration to minimize the risk of a collision at a CCP to a negligible level.

An overview of the adjustments can be found in Appendix A, while remaining adaption to the control strategy will be explained in the corresponding sections below. For the exact functioning of the VICS please refer to the article of Kamal et al. (2015).

3.2 Case study

The case provided by the UMCG Ambulancezorg is intersection Sontplein, displayed in Figure 3.1. This intersection is of importance to the UMCG Ambulancezorg, since it is part of the most used approach route towards the University Medical Center Groningen (UMCG). Firstly, for the sake of simplicity bicycle- and bus lanes were omitted in the simulation, pedestrians are excluded and therefore only regular traffic lanes with equal passenger vehicles are considered in the simulation. Secondly, it is assumed vehicles use the dedicated turn lanes per approach. In the simulation all lanes are combined per approach. Finally, the numbers per approach indicate the headings and are applied in the simulation. To reach the UMCG, vehicles should head towards approach 3. Approach 1 is the Europaweg which continues at approach 3 towards the UMCG and is expected to be the most used direction.

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15 3.3 Vehicle arrival generation

The first part of the simulation is the generation of equally sized vehicles. While arriving, the EV encounters vehicles driving at operating speed which are within 100 meters from crossing the intersection. Vehicles further away than 100 meter can pull over and do not affect the approaching EV and are therefore out of scope of this research. The vehicles are generated based on historic data collected from the municipality of Groningen. A discrete random distribution of vehicle arrival is generated in accordance to the article of Qin and Khan (2012). A random number between 0 and 1 is generated, when the number is below 0.5 no vehicle arrival is generated, while a number above 0.5 indicates a vehicle arrival. Finally, the model assumes vehicles use the dedicated turn lanes per approach.

3.4 Ambulance preemption

In the model of Kamal et al. (2015), an intersection control unit (ICU) takes control of the approaching CAVs and adjust their velocities, acceleration and deceleration. The process executed by the ICU is defined in the model predictive control (MPC) framework designed by Kamal et al. (2015). The MPC framework is designed in four components and is performed in successive order at each discrete time step. The first component determines the impact of the arriving vehicles on the intersection capacity. Based on the capacity, the second component controls the longitudinal movement of the new CAVs. The third component determines the cross-collision points (CCP) and determines the probability of a collision at the CCP. At last, the model predictive control (MPC) solves the constrained linear optimization problem per discrete time step to derive the optimal control to adjusts the velocities of the approaching vehicles. Completion of the MPC minimizes the risk of collision to a negligible level and ensures idle free safe crossing of the intersection. In case of emergency an additional constraint to the MPC framework must guarantee priority to the EV. The EV should be precluded from deceleration towards the CCPs and while crossing the intersection.

3.5 Data collection

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approach. The turn rates are determined based on the exact volumes per lane and are applied in the simulation of the FTC strategy. In the simulations performed by Kamal et al. (2015) the turning vehicles are not included and therefore cannot be applied in the preemption strategy of the VICS. However, the researchers provide three cases of lane capacities at different turns rates indicating a minor decrease in capacity at an average turn rate of 25% per approach, comparable to the turn rates displayed in Table 3.3. At last, it can be noted that the average volumes on approach 1 and 3 are substantially higher than the volumes on approach 2 and 4.

Table 3.1. Two phase signal timing plan for the fixed time control.

Green phase 1 Green phase 2 Yellow Red Cycle time

68 seconds 0 seconds 0 seconds 0 second 0 seconds

Table 3.2. One phase signal timing plan for the VICS.

Approach Average amount of vehicles per approach (veh/h) Average right turn Average left turn 1 827 20% 17% 2 399 30% 34% 3 765 12% 22% 4 356 35% 24%

Table 3.3. Vehicle intensities and turn rates based per approach.

4. SIMULATION

The control strategies will be simulated using the simulation software MATLAB (The MathWorks Inc., 2003). The first section provides performance indicators that will result from the simulations. In the second section, emergency vehicle detection is determined for the VICS strategy and the FTC strategy. Section three provides results from the VICS simulation and results from the FTC simulation. The last section provides insight in the applicability of the preemption strategy to the VICS.

4.1 Performance Indicators

The value of the VICS will be evaluated in comparison to the FTC strategy of Qin and Khan (2012), using the following performance indicators: more time needed, spare time, queue in vehicles per approach and discharged vehicles per approach. More time needed indicates a failure, since the control strategy does not have enough time to let the EV pass. A value more than zero indicates vehicles queue up in front of the EV and need to be discharged before the EV can cross the intersection. In case more time needed has a value of zero a queue may originate directly behind the EV if the queue on approach indicates a value above zero. The

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spare time indicates the time gap between the moment the queue ahead of the EV is cleared and the EV passes the intersection, which should be as low as possible to reduce inefficiency at the intersection. In case of the VICS, spare time will not be generated, since vehicles from all approaches are allowed to cross the intersection simultaneously. The control strategies are adapted by providing the number of discharged vehicles per approach as an additional performance indicator. This indicator will give insight in the vehicle throughput of the intersection.

4.2 Emergency vehicle detection distance

Arriving EVs need to be detected in order to activate the VICS preemption strategy. Due to V2X communication, EV detection is not limited by any distance. Consequently, to ensure smooth traffic flow, vehicles adjust their velocity at 100 meters before crossing the intersection at all given densities (Kamal et al., 2015). Accordingly, the ICU will not take control of the vehicle if it is more than 100 meters away from the intersection. Hence, the EV detection distance in the VICS will be adjusted to 100 meters at all saturation levels of vehicle arrival.

Changes in road saturation levels affect the average velocity of crossing vehicles, a higher vehicle arrival rate leads to lower velocities. As a result of lower velocities, the time for the vehicles to cross the intersection in 100 meters slightly increases by a total time of 1.15 seconds in a capacity range from 830 to 1600 vehicles per hour per lane. However, it is assumed that the EV will experience no delay in the preemption strategy of the VICS. The performance of the vehicles in the VICS is adopted from the research of Kamal et al. (2015) and is shown in Table 4.1.

EV Priority Distance and velocity

Vehicles per hour per lane 830 1200 1600

EV detection distance (m) 100.00 100.00 100.00

Velocity to cross intersection within 100 meter (m/s)

16.67 15.63 13.99

Time to cross intersection within 100 meter (s)

6.00 6.40 7.15

EV speed to cross intersection within 100 meter (m/s)

16.67 16.67 16.67

Time EV to cross intersection within 100 meter (s)

6.00 6.00 6.00

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In contrast to EV detection in the VICS, the FTC strategy of Qin and Khan (2012) requires physical EV detection. To ensure the safe passage of EVs for each approach, the FTCs detection distance is set up with the average queue lengths under normal operation plus two times the standard deviation. The EV detection distances are determined based on average queue lengths (lq) and the signal timing plan (Table 3.1). Table 4.2 lists the EV detection distance. To prevent uncertainty between detection of the EV and arrival at the intersection the distances should be as low as possible. For the complete overview of the queue lengths per approach under different saturation levels, see Appendix B. The EV arrival at approach 1 is detected at 305 meters from the intersection in the FTC simulation.

EV arrival from

Approach 1 Approach 2 Approach 3 Approach 4

Average queue length in vehicles (lq)

6.4 2.6 5.7 2.6

EVD Distance (meter) EV speed = 15 m/s

305.54 204.19 287.43 204.19

EVD period (seconds) 20.37 13.61 19.16 13.61

Table 4.2. Emergency vehicle detection distance for the FTC.

4.3 Simulation of VICS and FTC

The value of the VICS will be evaluated in comparison to the fixed-time control strategy of Qin and Khan (2012), using the analyzed traffic intensities collected from the municipality of Groningen. The first simulations of both strategies are based on the current two phase signal timing plan by the municipality of Groningen. Next, the preemption strategy of the VICS is simulated using the one phase signal timing plan, as listed in Table 3.2. At last, the VICS is simulated with a time period of six seconds. This provides insight in the number of discharged vehicles during the time it takes an EV to cross the intersection.Building upon the signal timing plans and the EV detection distances, different scripts are designed for the simulation of VICS and FTC. Each scenario is simulated 6 cycles.

4.3.1 Simulation of fixed time control (FTC)

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Scenario EV entry moment Time

1 At the beginning of green time (main road) t = 0 s

2 Middle of green light (main road) t = 17 s

3 The beginning of yellow light (main road) t = 34 s

4 The beginning or red light (main road) t = 37 s

5 Beginning of green light (side road) t = 38 s

6 Middle of green light (side road) t = 55 s

Table 4.3. Emergency vehicle arrival scenarios for VICS and FTC.

The FTC strategy is simulated first, with a fixed time period of 18 seconds as is applied in the current situation of Sontplein (Ijedema, 2018) and the two-phase signal timing plan as listed in Table 3.1. Results of the simulation are shown in Table 4.4. From Table 4.4 it may be concluded that the EV passes the intersection in each scenario without failure. Further, Table 4.4 shows that each scenario results in spare time, with the most spare time at scenario 2 and 3. High spare time indicates inefficiency at the intersection and a waste of time between the moment the queue is cleared and the EV arrives at the intersection. Last, results displayed in Table 4.4 show that the queue length at approach 4 is the lowest. An overview of the complete outcomes is presented in Appendix C.

Scenario More time needed (s)

Spare time (s) Discharged (veh)

Queue length (veh) per approach 1 2 3 4 1 0.00 9.42 0.00 0.00 0.83 0.83 0.33 2 0.00 18.00 5.67 0.67 1.17 1.33 0.17 3 0.00 18.00 6.50 1.83 1.83 1.33 0.00 4 0.00 16.98 6.50 1.83 2.00 1.33 0.33 5 0.00 16,98 6.50 1.83 2.00 1.33 0.33 6 0.00 13.38 6.50 1.67 0.17 0.83 0.17

Table 4.4. Average performance FTC.

4.3.2 Simulation of Vehicle-intersection coordination scheme (VICS)

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in Table 3.3. An overview of the complete outcomes of the simulated VICS can be found in Appendix D.

Scenario Failure Spare time (s)

Queue length (veh) per approach

Discharged (veh) per approach 1 2 3 4 1 2 3 4 1 0 0.00 0 0 0 0 0.00 0.00 0.00 0.00 2 0 0.00 0 0 0 0 4.17 1.50 5.00 1.16 3 0 0.00 0 0 0 0 5.83 1.67 6.67 1.33 4 0 0.00 0 0 0 0 5.83 1.67 6.67 1.33 5 0 0.00 0 0 0 0 5.83 1.67 6.67 1.33 6 0 0.00 0 0 0 0 5.83 1.67 6.67 1.33

Table 4.5 Average performance VICS (speed EV = 16.67 m/s, L = 100 m).

The following VICS simulation will apply the one phase signal timing plan of Table 3.2 which is designed to imitate the infinite green signal as is designed by Kamal et al. (2015). Equal to the previous simulations, the EV enters the intersection from approach 1 in the following different scenarios: at the beginning of green time, after six seconds of green time, after 10 seconds of green time, after 20 seconds of green time and consecutively after 40 and 68 seconds of green time. The scenarios are designed to give insight in the number of vehicles that are discharged during 68 seconds, which is comparable to the duration of an entire cycle of signal phases of the FTC strategy. Scenario 2 gives insight in the number of vehicles that are discharged during the time period an EV needs to cross the intersection. Scenario 5 and 6 provide insight in the number of vehicles that are additionally discharged in the extended green time op phase 1.

Scenario Entry moment Time

1 At the beginning of green time (main road) t = 0 s

2 After 6 seconds (main road) t = 6 s

6 At the end of green time (main road) t = 68 s

Table 4.6. Scenarios for the emergency vehicle arrival for 68 seconds VICS.

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simulation displayed in Table 4.5, it is shown in Table 4.7 that the number of discharged vehicles keeps increasing after 34 seconds, since green time is extended. The complete overview of the performance of the VICS simulation as presented in Table 4.7 is presented in Appendix E.

Scenario Failure Spare time (s)

Queue length (veh) per approach

Discharged (veh) per approach 1 ₂ ₃ ₄ ₁ ₂ ₃ ₄ 1 0 0.00 0 0 0 0 0.00 0.00 0.00 0.00 2 0 0.00 0 0 0 0 1.00 0.17 0.50 0.00 3 0 0.00 0 0 0 0 1.17 0.17 0.50 0.17 4 0 0.00 0 0 0 0 3.83 0.50 3.00 0.67 5 0 0.00 0 0 0 0 4.83 0.67 3.83 1.00 6 0 0.00 0 0 0 0 8 1.83 6.17 1.83

Table 4.7 Average performance VICS (speed EV = 16.67 m/s, L = 100 m).

4.4 Preemption allocation determination 4.4.1 Determination of discharged vehicles

Table 4.1 demonstrates that an EV located 100 meters from the intersection needs 6 seconds to pass it. Therefore, EV arrival entry of a VICS will be simulated with a total cycle time of 6 seconds. The simulation is performed under increasing intensities from 800 until 1600 vehicles per hour per lane. Each of the seven scenarios represent a number of consecutive seconds, starting from 0, as listed in Table 4.8. When the number of discharged vehicles exceeds 1, it implies there will be an additional vehicle in the same time frame as the EV on the same lane.

Scenario Entry moment Time

1 At the beginning of the VICS t = 0 s

2 After 1 second t = 1 s

3 After 2 seconds t = 2 s

7 At the end of the VICS t = 6 s

Table 4.8. Different scenarios of emergency vehicle arrival.

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Vehicle arrival saturation per hour

Scenario Entry moment Time 800 1000 1200 1400 1600

1 At the beginning of the VICS t = 0 s 0.00 0.00 0.00 0.00 0.00

2 After 1 second t = 1 s 0.50 0.50 0.67 0.67 1.17

3 After 2 seconds t = 2 s 0.50 0.67 1.00 1.00 1.50

4 After 3 seconds t = 3 s 1.00 0.67 1.00 1.33 1.83

5 After 4 seconds t = 4 s 1.00 1.00 1.33 1.83 2.00

6 After 5 seconds t = 5 s 1.17 1.17 1.67 2.00 2.50

7 At the end of the VICS t = 6 s 1.17 1.17 1.67 2.33 2.50

Table 4.9. Discharged vehicles at approach 1 per different saturation levels.

Since the results displayed in Table 4.9 show that more than one vehicle is discharged, it needs to be determined whether vehicles crossing the intersection from the same lane as the EV, block the way. As explained in section 2.3, the ICU orders vehicles to accelerate and decelerate to pass the intersection. To ensure smooth traffic flow, the acceleration of the approaching vehicles is limited to 5 m/s2 and a deceleration is limited to – 6m/s2 (Kamal et al., 2015). Therefore, it is possible that a preceding vehicle to the EV is ordered to decelerate and blocks the way of the EV which is precluded from deceleration. In that case the preceding vehicle should have priority as well to cross the intersection at the same velocity as the EV and will not block the way.

4.4.2 Velocity predecessor

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Figure 4.1: Equation to determine velocity of predecessor.

The equation is solved by three variables. The first variable, 𝑉𝐸𝑉 is the velocity of the emergency vehicle in meter per second, which in this paper is set at 16,67 m/s. Secondly, the heading distance between the EV and its predecessor, denoted as ℎ, is extracted from the minimum observed distance in the vehicle arrival generation which is also used to determine the EV detection distance. The determined heading distance is displayed in Table 4.10. At an arrival of 830 vehicles per hour, the minimum observed time difference in vehicle arrival is 2,168 seconds and is equal to a heading distance of 36,14 meters at an operating speed of 16.67 m/s. An arrival of 1600 vehicles per hour per lane results in a minimum observed heading distance of 18,75 meters. At last, the blocking distance (𝑑) is 100 meters, since the EV and the preceding vehicle cannot change lanes during this path. The outcome indicates the velocity of the predecessor in meter per second (𝑉𝑃). As long as the velocity of the predecessor is higher than the given value (𝑉_𝑃) it will not block the way of the EV. In case the velocity of the predecessor falls below the given value (𝑉_𝑃) the predecessor may block the way of the EV.

Table 4.10. Minimum heading distance per saturation level from approach 1.

Heading distance per saturation level

Vehicles per hour 830 1000 1200 1400 1600

Minimum observed gap in seconds

2,17 1,80 1,50 1,28 1,13

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Figure 4.2 displays the lowest observed vehicle velocities, obtained from the VICS simulation in the research of Kamal et al. (2015). Figure 4.1 presents the plotted equation to determine the critical velocity of the predecessor (𝑉𝑃) to be assigned with priority. The complete overview of critical velocity (𝑉_𝑃) can be found in Appendix G. From Figure 4.2, it can be seen that the lowest observed speed in the VICS decreases as the heading distance increases.

Figure 4.2. Speeds of predecessor to be assigned with priority.

From the research of Kamal et al. (2015) can be concluded that the distribution of velocity and acceleration deviates during the path of 100 meters for vehicles crossing the intersection. By subtracting the heading distance from the total blocking distance of 100 meters, the velocities per vehicle with respect to the distance from crossing the intersection are adopted from the research of Kamal et al. (2015). The complete overview of the observed velocities can be found in Appendix G. 9 10 11 12 13 14 15 16 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Sp ee d o f p red ec ess o r (m /s ) Heading distance (m)

Critical speed of predecessor to be assigned priority

Lowest observed speed VICS

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5. DISCUSSION

In this chapter the results of the study are discussed with reference to the research question of the study, which was: How will a preemption strategy in a vehicle-intersection coordination scheme affect the ambulance response time without compromising on safety and intersection efficiency? The first section discusses normal intersection operation without arrival of an EV with the VICS and the FTC. In the second section the efficiency and safety performance of the VICS are discussed in comparison to the FTC. Section three addresses the practical applicability of the preemption strategy. The fourth section discusses the enhanced response time, safety and efficiency of the VICS. Finally, an overview will be presented of limitations to this research.

5.1 Normal operation without EV arrival

Figure 5.1 presents the data from the simulation of a normal intersection operation under increasing vehicle arrival. Normal intersection operation indicates a complete signal traffic cycle without arrival of an EV. The value of the VICS can be seen by the constant absence of queues. The queue length of the VICS remains zero in all vehicle arrival intensities, since the scheme ensures idling free flow. Implementing a VICS would achieve great benefit to all traffic at the intersection.

5.2 VICS efficiency at the intersection

The results presented in the following figures show the efficiency performance of the two simulations and are discussed accordingly. In both figures, higher results suggest a less efficient preemption strategy.

First, the lost time after EV detection is presented by the spare time. From Figure 5.2 it can be seen that the VICS operates completely without spare time. Further, it can be seen in

0 2 4 6 8 10 12 14 400 830 1000 1200 1400 1600 Qu eu e len g th in v eh icles

Arrival in vehicles per hour per approach

Queue normal operation

FTC Approach 1 FTC Approach 2 FTC Approach 3 FTC Approach 4 VICS

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scenario 2 and 3 that the fixed time of 18 seconds leads entirely to spare time. The FTC strategy as applied is highly inefficient. The preemption strategy of the VICS is able to reduce the spare time completely, since vehicles from all approaches are coordinated to cross the intersection simultaneously and no time will be lost.

Figure 5.2: Lost time per scenario from approach 1

Further, the value of the VICS is demonstrated by the average queue length in vehicles. Figure 5.3 summarizes the averaged queue lengths of the FTC, as given in Table 4.4. As shown in Figure 5.3, the preemption strategy in the VICS is successful in eliminating queue lengths on all approaches. Compared to the FTC, the VICS reduces the queues from an average of all approaches from 1.15 vehicles to 0 vehicles. The average queue lengths per approach indicate efficiency at the intersection and is depicted in Figure 5.3. A lower queue indicates a more efficient operation of the intersection. Noticeable is the relative high queue length of approach 2 in the FTC, despite the lower average vehicle arrival as displayed in Table 3.3. It is expected to be an exception. The length of queues in vehicles of approach 1 and 3 are conform the average amount of vehicles per approach as shown in Table 3.3. In conformity to the normal operation of the VICS, the preemption strategy of the VICS generates no queue and therefore is more efficient. 0 4 8 12 16 20 1 2 3 4 5 6 Sp ar e tim e (s ) Scenario

Lost time after EV detection

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Figure 5.3: Average queue per approach for arrival of EV from approach 1

Lastly, it is observed from Table 4.5 and 4.7 that the distribution of discharged vehicles is spread out more in case the VICS simulation has more green time as displayed in Table 4.7. The additional time offered in the signal timing plan for the VICS is used to discharge vehicles with a more equal distribution. Further, the additional time can be used to discharge more vehicles as displayed in Table 4.7. The increase in discharged vehicles and the more equal distribution per scenario displays the added value of the infinite green time as designed by Kamal et al. (2015).

5.3 Application of priority

By simulation of the adapted real-time control model of Qin and Khan (2012) the preemption strategy in a VICS has been proven to be successful. Elaborating on this success, it is determined if additional vehicles to the EV should be assigned with priority.

From table 4.9 can be seen that more than one vehicle discharge during the 6 seconds it takes an EV to cross the intersection. However, as can be seen from Figure 4.2, the lowest observed speed of the vehicle that is discharged additionally to the EV is sufficient to stay ahead of the EV. Further, it can be seen from Figure 5.4 that the number of discharged vehicles per approach at a saturation range from 830 to 1600 vehicles per hour per lane is more than two after 4 and 5 seconds. However, the heading distance is too large to become an obstruction to the EV after the fourth second. At last, the article of Kamal et al. (2015) assume equally distributed vehicle arrival. An equal arrival interval can be set by a distance large enough to prevent vehicles from blocking the way for an EV by CACC (Zohdy & Rakha, 2016).

0 0,4 0,8 1,2 1,6 2 1 2 3 4 Qu eu e in v eh icles Approach

Average queue lengths for EV from approach 1

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Figure 5.4: Discharged vehicles per saturation level from approach 1

5.4 VICS safety, response time and efficiency

In conclusion, the results indicate that the EV preemption strategy for the VICS can ensure safe and idling free crossing of the intersection, while enhancing the total intersection efficiency for all vehicles at saturations varying between 0 and 1600 vehicles per hour per lane.

First of all, safety of the ambulance and other road users will be improved by 100% by the scheme of Kamal et al. (2015). Collision free crossing of intersections is guaranteed by V2X communication of CAVs. By applying the preemption strategy of the VICS, EVs will never have to cross intersections anymore while vehicles from other approaches are given a green signal. This will eliminate the 38% of the accidents with ambulances happened to be the cause between 2010 and 2015.

Next, the response time will improve significantly as well. Zero queues, higher speeds, increased intersection capacity and communicating vehicles will provide a safe and clear pathway to the EV and reduce response time in the EV preemption strategy of the VICS.

At last, impact on traffic will be minimized to a negligible level. The VICS preemption strategy reduces spare time and queues completely to zero as can be seen from the results presented in Figure 5.2 and Figure 5.3. The VICS preemption strategy does not only minimize impact on other traffic, it enhances intersection efficiency to all road users compared to the current normal operation as shown in Figure 5.1.

0 0,5 1 1,5 2 2,5 3 0 1 2 3 4 5 6 Dis ch ar g ed v eh icles Seconds

Discharged vehicles per saturation level

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The strong improvement can be explained by the fact that the VICS relies on CAVs which are expected until 2025 at earliest.

5.5 Limitations of research

A last note of criticism is given to the results. An improvement in safety, speed and efficiency is likely by implementing a priority strategy in VICS. But to operate successfully it is assumed that all needed technology operates without failure and delay. Secondly, the article of Qin and Khan (2012), Kamal et al. (2015) and this research have simplified reality in favor of practicality of the research. Therefore, the simulation of the VICS conducted in this research is performed by an adapted real-time control strategy of Qin and Khan (2012). Changes in design of the control strategy are made to imitate the efficient operation of the VICS. The actual coordination scheme as proposed by Kamal et al. (2015) is not simulated.

Besides, the research assumes priority can be arranged by a constraint. However, the constraint needs to be developed in future research. To realize a preemption strategy in the design of the VICS of Kamal et al. (2015), the model should be improved to distinguish an EV from regular vehicles. It would increase academic relevance to modify the actual constrained nonlinear optimization problem of Kamal et al. (2015) and perform the VICS simulation by the same tools as applied by the researchers. Apart from limitations of the actual performed simulation, the vehicle intensities are also a limitation. Only data of one week in 2018 is collected and may therefore not be entirely representative.

6. CONCLUSION

Organizing Sontplein by a VICS will substitute traffic lights by algorithms to coordinate the crossing vehicles and approaching EVs. Implementation of the VICS relies on full penetration of CAVs and will ensure to solve inconveniences such as collisions, waiting time and delay. Traffic flow be improved, queues at all approaches will be eliminated resulting in lower response times. Because of the lower response times, the survival rate and recovery process of patients will significantly improve. The preemption strategy will not only affect the patient survival rate, but also minimizes impact on other road users by ensuring idling free flow to all vehicles. This results in a saving of travel time for all operating vehicles and increases efficiency at the bottlenecks of the Dutch infrastructure.

6.1 Managerial and societal considerations to prepare for CAVs

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controllers manage all approaching airplanes within a certain distance from landing. The air traffic controllers direct the airplanes to descend by a given angle and velocity to ensure a safe and idling free landing with enough heading distance to other approaching airplanes. The job performed by the air traffic controllers bears a close remblance to the optimization problem of the ICU.

To solve infrastructure bottlenecks of the Dutch infrastructure, the Dutch national government budgeted 957 million euro for 2018 (Government of the Netherlands, 2018). Cost of road safety, environment and economic damage due to congestion are budgeted for 27 billion for 2018 by the Dutch Ministry of Infrastructure and Water Management (Mobiliteitsbeeld, 2017). Furthermore, Mobiliteitsbeeld (2017) shows that almost all projects planned until 2030 are aimed at solving infrastructure bottlenecks and increase throughput by adding at least 1000 kilometers of lanes until 2030. The data suggest that instead of only adding kilometers of lanes, governments and organizations should not overlook anticipating emerging CAVs. Since partial penetration is proven to improve traffic flow and safety on roads and intersections, the UMCG Ambulancezorg should not await full adoption in 2050, but adopt CAVs as soon as possible and take advantage in terms of increased safety and improved response time.

I argue to cut on infrastructure budget and increase investment in a communicating autonomous fleet. In addition to this papers research, air traffic has already proven to increase capacity and safety of lanes by communication with a centralized controller. It is time to increase intersection capacity and safety by use of the Intersection Control Units and Connected Autonomous Vehicles.

6.2 Future research

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APPENDIX A Input Simulation APPENDIX A.1 Input Simulation VICS

Adapted parameter VICS script Value

Saturation 3250 vehicles/hour

Lost time per phase 0 seconds

All red time 0 seconds

Lost time per cycle 0 seconds

Spacing between cars 2 metres

Safety time interval 0

Sartup delay 0

Discharge speed 17.2

APPENDIX A.2 Input Simulation FTC

Parameter FTC Value

Saturation 2000 vehicles/hour

Lost time per phase 3 seconds

All red time 2 seconds

Lost time per cycle 8 seconds

Spacing between cars 8 metres

Safety time interval 3

Sartup delay 2

Discharge speed 4.44

APPENDIX A.3 . Signal timing plan FTC

APPENDIX A.4 . Signal timing plan FTC

68 seconds 0 seconds 0 seconds 0 second 0 seconds

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APPENDIX B Average Queue Length Under Normal Operation

Table B.1 Average queue length under normal operation at approach 1 at different vehicle arrivals

Approach 1 1 1 1 1 1 Vehicle arrival 400 830 1000 1200 1400 1600 2 1 2 5 6 7 1 4 4 5 5 5 3 3 4 1 6 6 2 1 4 4 6 5 2 4 3 4 7 10 3 3 2 5 5 9 2 3 5 4 10 7 1 5 6 7 6 8 0 5 5 4 8 8 1 2 7 6 6 8 2 3 4 7 9 6 2 6 5 8 6 9 2 1 5 5 5 11 2 2 2 5 9 9 2 5 7 5 5 7 3 5 4 8 8 7 3 4 5 6 7 10 1 6 4 7 8 9 4 2 6 5 10 8 3 3 5 5 11 6 Average 2,05 3,4 4,45 5,3 7,15 7,75 Average + 2 * SD 3,89 6,52 7,31 8,47 10,80 11,03

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0 3 1 5 2 4

Average 1,05 2,15 2,4 3,4 3,85 4,65

Average + 2 * SD 2,78 4,18 5,39 5,80 6,77 7,63

Approach 3 3 3 3 3 3 Vehicle arrival 500 830 1000 1200 1400 1600 1 4 5 5 5 7 2 6 4 3 5 7 3 2 6 6 5 7 1 4 4 6 6 8 2 2 4 6 7 8 2 3 5 8 2 8 1 4 6 2 7 11 1 4 2 6 9 3 3 5 4 7 4 4 1 1 3 6 6 7 2 5 6 3 7 9 2 3 5 3 9 6 1 5 3 6 3 9 2 3 4 7 4 7 3 1 4 3 8 8 1 3 5 4 7 7 1 1 7 6 8 9 2 3 2 3 6 11 2 3 3 5 3 10 1 5 2 5 3 6 Average 1,7 3,35 4,2 5 5,7 7,6 Average + 2 * SD 3,13 6,20 7,00 8,29 9,75 11,52

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40 2 3 3 4 0 5 0 3 1 2 6 5 0 4 3 4 4 5 1 2 4 1 6 5 0 2 1 4 5 4 Average 0,9 2,55 2,6 2,95 3,9 4,1 Average + 2 * SD 2,56 4,78 5,16 5,09 7,18 6,78

Table B.4 Average queue length under normal operation VICS

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APPENDIX C Results Of Fixed Time Control Strategy

Scenario Cycle

Spare

time (s) Queue on approach

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APPENDIX D Results Vehicle-Intersection Coordination Scheme With Traffic Signal Phases

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APPENDIX E Results Vehicle-Intersection Coordination Scheme

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APPENDIX F Results Highly Dense Traffic

Scenario Cycle Discharged vehicles at approach 1 per saturation

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APPENDIX G Adopted Velocity Distribution

ℎ 𝑉_𝑃 (m/s) Lowest observed speed of predecessor