ENSEMBLE WP 4 Infrastructure and logistics WP 4

ENSEMBLE WP 4 - Infrastructure and logistics

WP 4. 1. Requirements from Infrastructure

WP 4. 1. Requirements from infrastructure Assessment of the impact of multi-brand platooning on: existing road infrastructure (pavement, bridges, tunnels) Assessment of the variability of these impacts, because of the multibrand characteristics of the platoon Variability in loads and dimensions Subtask 4. 1. 1: Impacts of platoons on pavements Subtask 4. 1. 2: Impacts of platoons on bridges and tunnels Subtask 4. 1. 3: Trial on-road assessment of platoon impact on infrastructure

Subtask 4. 1. 1: Impacts of platoons on pavement Pavement design with platoons : Methodology 140 Instrumentation on site Several configurations of platoons Characteristics of Heavy Vehicle (speed, distance between vehicles, etc. ) Loads, Cumulated traffic Life duration prediction Objectif 1 Déformation (µm/m) 100 60 20 -20 0 0. 1 0. 2 0. 3 0. 4 Signal longitudinal Signal transversal Induced strain (shape, intensity, etc. ) Wandering + Miner law εmax = … Np = … n = … D=… Signal processing / Calculation of different parameters For a HV, Pavement Design Tool Alizé ou Visco. Route© 0. 5 Temps (s) Fatigue law (new model) Experimental program in Lab. Reproduce in Lab. different configurations of platoons

Subtask 4. 1. 1: Impacts of platoons on pavement Test program with 3 heavy vehicles Measurements to evaluate the impact of platooning on pavement : on site monitoring Different case will be tested with each trucks apart and the with the 3 truck platoon 2 different temperatures : < 20°C (Mars 2019) and T 30°C (June – September 2019) 3 different speeds (60 km/h, 75 km/h and 90 km/h) Distances D between the trucks function of the speed (about 0. 5 second gap) KPI to be measured: Strain accumulation due to short rest periods between the load cycles Reduced lateral wandering compared with series of single vehicles KPI to be derived: Lifetime: platoons vs series of single vehicles

Subtask 4. 1. 2 Impact of platoons on bridges and tunnels Platoons on bridges: vertical forces Longitudinal effect: more trucks on a span Transversal effect: aligned wheel paths KPI to be measured: maximum stress under passage of a platoon, at various lateral positions KPI to be derived: - Reduction in bridge lifetime due to platooning. - In case of stopped traffic flow, increase of traffic queue (congestion).

Subtask 4. 1. 2 Impact of platoons on bridges and tunnels Platoons and tunnels: calorific volume, traffic management issue More trucks inside a tunnel Þ Higher calorific volume (payload) Þ Various traffic management procedures: Given (maximum) number of trucks in the tunnel Stop other traffics at given times for truck passing • KPI to be measured: - Travel time to get through the tunnel - (Traffic management procedure) Spacing, driving speed through the tunnel - Quality of V 2 X and I 2 V communication

Task 4. 3. Economic and environmental Benefits of multi -brand Truck Platooning

Objectives of T 4. 3 • Economic and business model – Understand the drivers’ behaviour and platooning market share – Contribute to design the platooning process – Prefigure some business models – Anticipate regulatory questions: • How does platooning fit in national freight transport policies? • Are there infrastructure investment needs, where and how much? • Is subsidy needed/justified? • Environmental impact – Confirm downsize fuel and GHG emission savings estimates – Measure other pollutants – Measure LCA impacts

T 4. 3. 1 Economic and business models • Problem statement Decision to equip a vehicle with platooning technology • Economic as a two-stage game • The decision to equip a HGV for platooning • The decision to use the platooning service • Value and costs of platooning • Savings on fuel consumption and possibly other sources (to confirm) • Platooning is a synchronisation effort, it entails a cost • This cost is not the same for platooning on the fly and scheduled platooning • Group’s effect Decision to form platoons • The value of the service depends on the number of users • Spatial dimension • Route choice is relevant

T 4. 3. 1 Approach : ad hoc Model Po. C (not calibrated, orders of magnitudes are meaningless) • Platooning value increases (and cost decreases) with trip length and traffic density • It’s a fixed point problem: the more users, the higher the value - virtuous (or vicious? ) circle • Share of equipped trucks and share of platooning trucks are different

Task 4. 4. Impact on Truck Drivers and other Road Users (M 3 -M 30)

Subtask 4. 4. 1: Impact on other road users • Objectives • Identification of possible issues for car drivers while meeting a platoon • Test possible countermeasures to improve road safety and platoon acceptance by other road users • Use cases studied on a car driving simulator • 3 manoeuvers: motorway entrance/exit and HGV overtaking • 2 platoon lengths (3 and 7 trucks) • 2 levels of traffic (high/low) • 2 spacing gaps (10 m and 15 m)

Subtask 4. 4. 2: Understanding the convoy use to forecast the platooning adoption by truck drivers • Objectives 1. Identifying the variables preceding the platoon forming: Ø Why does a truck driver decide to follow the truck in front of it? Ø When does he begin to adapt its driving to the preceding vehicle? 2. Specifying the strategies developed by the drivers to enter into a platoon, to stay in it (keep the spacing) or to leave it: Ø How does he regulate the speed of its truck, and its spacing? Ø What are the effects of the environment? 3. Foreseeing the impacts of multi-brand platooning on truck drivers’ activity. 4. Giving insight in the management interacting trucks involved in the future platoons.

Subtask 4. 4. 2: cont’n • Steps 1. State of art on driving in a platoon and car-following situations for truck drivers, autonomous vehicles and their impact on the travellers 2. Identification and establishment of contact with road transport companies 3. Naturalistic driving approach ü Development of a system combining two features/tools: i) measuring the truck speed and spacing, and ii) recording these parameters by video ü On-board observations (in trucks) in real situations during complete round trips on a French motorway (A 1 or A 10) ü Interviews with drivers ü Analysis of platooning practice in the truck driving activity

Subtask 4. 4. 2: cont’n • Fields France: motorway A 1 Paris to Lille Busiest French motorway • Length 211 km Made of: • Three lanes per direction, • 25 exits, • 11 junctions, • 10 rest areas (northbound) and 8 (southbound), • 6 gas stations (northbound) and 7 (southbound), • 2 toll gates and 1 tunnel (1, 400 m) Ø distance between rest areas (or gas stations) from 2 to 27 km (12 km in average) France: A 10 motorway Paris to Bordeaux Longest motorway in France • Length 543 km Made of: • Three/two lanes per direction, • 49 exits, • 12 junctions, • 22 rest areas • 15 gas stations • 4 toll gates Ø distance between rest areas from 1 to 33 km

Subtask 4. 4. 2: end • KPIs on acceptability conditions of platooning by truck drivers: – – – Driving experience (beginner vs experienced driver) Level of knowledge of the journey Type of infrastructure (number of lanes, exits, junctions…) Hour of the day (daylight driving vs night driving) Traffic conditions (free-flow traffic vs dense traffic, hazards) Time constraints on the driver (delays, required arrival time by the shipper or the customer, unexpected event reducing the room of manoeuver) – … • Observations won’t be able to assess the effect of these parameters on platooning. However, should we underline of them on the driving strategies and the platooning practice by the driver?

4. 5. Impact on Traffic Flow

Subtask 4. 5. 1. Methodology, proposed framework Deployment Impact assessment Definitions Network Demand Traffic Control Simulation analysis User cases Truck specs. Baseline Data • Sizing • KPI • What if? Verification & calibration Specifications Baseline Data - Parameter - Models - Use cases Field Tests Scaling-up - User case - Network Statistics, Analytics - Similarities - Indicators Platoon Control Simulation-based Impact assessment Data Pre-deployment Deployment Full implementation

Subtask 4. 5. 2. Traffic Impact - KPIs Dynamic performance & operation Stability of traffic flow • Measure and characterize dynamically a platoon formation in a heterogeneous framework (multi-brand). E. g. stability of the time gap between trucks, acceleration profiles for specific maneuvers. Impact on traffic flow & other users Impact on road capacity • Determine the impact of truck platoon on other road users at traffic level. This can be characterized by a greater probability to overtake, increasing relative speed with respect to the platoon. Platoon behavior to maneuvers Impact on road users + flow stability - Specific maneuvers can impact traffic behavior such as, joining a new platoon, dissolving an existing platoon, reaction to insertion maneuvers from external drivers.

Subtask 4. 5. 3. Model-based specification & assessment WP 2 : TNO Specification TNO LICIT WP-T 4. 5 WP 3 : OEM Algorithms integration Control algorithms Integration in Simulator WP 5 : Final demonstration Fo. T Use case def. Analysis IDIADA Experience from previous research • Projects on CAV • Academic research in Traffic Flow Theory WP 4. 4