Platoons of connected vehicles can double throughput in urban roads

Intersections are the bottlenecks of the urban road system because an intersection’s capacity is only a fraction of the maximum flows that the roads connecting to the intersection can carry. This capacity can be increased if vehicles cross the intersections in platoons rather than one by one as they do today. Platoon formation is enabled by connected vehicle technology. This paper assesses the potential mobility benefits of platooning. It argues that saturation flow rates, and hence intersection capacity, can be doubled or tripled by platooning. The argument is supported by the analysis of three queuing models and by the simulation of a road network with 16 intersections and 73 links. The queuing analysis and the simulations reveal that a signalized network with fixed time control will support an increase in demand by a factor of (say) two or three if all saturation flows are increased by the same factor, with no change in the control. Furthermore, despite the increased demand vehicles will experience the same delay and travel time. The same scaling improvement is achieved when the fixed time control is replaced by the max pressure adaptive control. Part of the capacity increase can alternatively be used to reduce queue lengths and the associated queuing delay by decreasing the cycle time. Impediments to the control of connected vehicles to achieve platooning at intersections appear to be small.     

Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

In this paper, a forward power-train plug-in hybrid electric vehicle model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Using wirelessly communicated predictive traffic data for vehicles in a roadway network, as envisioned in intelligent transportation systems, traffic prediction cycles are optimized using a cycle optimization strategy. This resulted in a 56-86% fuel efficiency improvements for conventional vehicles. When combined with the plug-in hybrid electric vehicle power management system, about 115% energy efficiency improvements were achieved. Further improvements in the overall energy efficiency of the network were achieved with increased penetration rates of the intelligent transportation assisted enabled plug-in hybrid electric vehicles.     

Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic

Connected Vehicles (CV) equipped with a Speed Advisory System (SAS) can obtain and utilize upcoming traffic signal information to manage their speed in advance, lower fuel consumption, and improve ride comfort by reducing idling at red lights. In this paper, a SAS for pre-timed traffic signals is proposed and the fuel minimal driving strategy is obtained as an analytical solution to a fuel consumption minimization problem. We show that the minimal fuel driving strategy may go against intuition of some people; in that it alternates between periods of maximum acceleration, engine shut down, and sometimes constant speed, known in optimal control as bang-singular-bang control. After presenting this analytical solution to the fuel minimization problem, we employ a sub-optimal solution such that drivability is not sacrificed and show fuel economy still improves significantly. Moreover this paper evaluates the influence of vehicles with SAS on the entire arterial traffic in micro-simulations. The results show that SAS-equipped vehicles not only improve their own fuel economy, but also benefit other conventional vehicles and the fleet fuel consumption decreases with the increment of percentage of SAS-equipped vehicles. We show that this improvement in fuel economy is achieved with a little compromise in average traffic flow and travel time.     

Evaluation of a model predictive control framework for motorway traffic involving conventional and automated vehicles

A Model Predictive Control (MPC) strategy for motorway traffic management, which takes into account both conventional control measures and control actions executed by vehicles equipped with Vehicle Automation and Communication Systems (VACS), is presented and evaluated using microscopic traffic simulation. A stretch of the motorway A20, which connects Rotterdam to Gouda in the Netherlands, is taken as a realistic test bed. In order to ensure the reliability of the application results, extensive speed and flow measurements, collected from the field, are used to calibrate the site’s microscopic traffic simulation model. The efficiency of the MPC framework, applied to this real sizable and complex network under realistic traffic conditions, is examined for different traffic conditions and different penetration rates of equipped vehicles. The adequacy of the control application when only VACS equipped vehicles are used as actuators, is also considered, and the related findings underline the significance of conventional control measures during a transition period or in case of increased future demand.     

An eco-driving system for electric vehicles with signal control under V2X environment

The benefit of eco-driving of electric vehicles (EVs) has been studied with the promising connected vehicle (i.e. V2X) technology in recent years. Whereas, it is still in doubt that how traffic signal control affects EV energy consumption. Therefore, it is necessary to explore the interactions between the traffic signal control and EV energy consumption. This research aims at studying the energy efficiency and traffic mobility of the EV system under V2X environment. An optimization model is proposed to meet both operation and energy efficiency for an EV transportation system with both connected EVs (CEVs) and non-CEVs. For CEVs, a stage-wise approximation model is implemented to provide an optimal speed control strategy. Non-CEVs obey a car-following rule suggested by the well-known Intelligent Driver Model (IDM) to achieve eco-driving. The eco-driving EV system is then integrated with signal control and a bi-objective and multi-stage optimization problem is formulated. For such a large-scale problem, a hybrid intelligent algorithm merging genetic algorithm (GA) and particle swarm optimization (PSO) is implemented. At last, a validation case is performed on an arterial with four intersections with different traffic demands. Results show that cycle-based signal control could improve both traffic mobility and energy saving of the EV system with eco-driving compared to a fixed signal timing plan. The total consumed energy decreases as the CEV penetration rate augments in general.     

Isolated intersection control for various levels of vehicle technology: Conventional,connected, and automated vehicles

Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection.Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology.     

Charging strategies for economic operations of electric vehicles in commercial applications

When substituting conventional with electric vehicles (EV) a high annual mileage is desirable from an environmental as well as an economic perspective. However, there are still significant technological limitations that need to be taken into consideration. This study presents and discusses five different charging strategies for two mobility applications executed during an early stage long-term field test from 2013 to 2015 in Germany, which main objective was to increase the utilization within the existing technological restrictions. During the field test seven EV drove more than 450,000 km. For four out of five presented charging strategies the inclusion of DC fast charging is indispensable. Based on the empirical evidence five key performance indicators (KPI) are developed. These indicators give recommendations to economically deploy EV in commercial fleets. The results demonstrate that the more predictable the underlying mobility demand and the more technical information is available the better the charging strategies can be defined. Furthermore, the results indicate that a prudent mix of conventional and DC fast charging allows a high annual mileage while at the same time limiting avoidable harmful effects on the battery.     

Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system

As electric vehicles (EVs) have gained an increasing market penetration rate, the traffic on urban roads will tend to be a mix of traditional gasoline vehicles (GVs) and EVs. These two types of vehicles have different energy consumption characteristics, especially the high energy efficiency and energy recuperation system of EVs. When GVs and EVs form a platoon that is recognized as an energy-friendly traffic pattern, it is critical to holistically consider the energy consumption characteristics of all vehicles to maximize the energy efficiency benefit of platooning. To tackle this issue, this paper develops an optimal control model as a foundation to provide eco-driving suggestions to the mixed-traffic platoon. The proposed model leverages the promising connected vehicle technology assuming that the speed advisory system can obtain the information on the characteristics of all platoon vehicles. To enhance the model applicability, the study proposes two eco-driving advisory strategies based on the developed optimal control model. One strategy provides the lead vehicle an acceleration profile, while the other provides a set of targeted cruising speeds. The acceleration-based eco-driving advisory strategy is suitable for platoons with an automated leader, and the speed-based advisory strategy is more friendly for platoons with a human-operated leader. Results of numerical experiments demonstrate the significance when the eco-driving advisory system holistically considers energy consumption characteristics of platoon vehicles.     

Modelling energy consumption of electric freight vehicles in urban pickup/delivery operations: analysis and estimation on a real-world dataset

Electric Freight Vehicles (EFVs) are a promising and increasingly popular alternative to conventional trucks in urban pickup/delivery operations. A key concerned research topic is to develop trip-based Tank-to-Wheel (TTW) analyses/models for EFVs energy consumption: notably, there are just a few studies in this area. Leveraging an earlier research on passenger electric vehicles, this paper aims at filling this gap by proposing a microscopic backward highly-resolved power-based EFVs energy consumption model (EFVs-ECM). The model is estimated and validated against real-world data, collected on a fleet of five EFVs in the city centre of Rome, for a total of 144 observed trips between subsequent pickup/delivery stops. Different model specifications are tested and contrasted, with promising results, in line with previous findings on electric passenger vehicles.     

Hierarchical model predictive control for multi-lane motorways in presence of Vehicle Automation and Communication Systems

A widespread deployment of vehicle automation and communication systems (VACS) is expected in the next years. This may lead to improvements in traffic management efficiency because of the novel possibilities of using VACS both as sensors and as actuators, as well as of a variety of new communications channels (vehicle-to-vehicles, vehicle-to-infrastructure) and related opportunities. To achieve this traffic flow efficiency, appropriate studies, developing potential control strategies to exploit the VACS availability, are essential. This paper describes a hierarchical model predictive control framework that can be used for the coordinated and integrated control of a motorway system, considering that an amount of vehicles are equipped with specific VACS. The concept employs and exploits the synergistic (integrated) action of a number of old and new control measures, including ramp metering, vehicle speed control, and lane changing control at a macroscopic level. The effectiveness and the computational feasibility of the proposed approach are demonstrated via microscopic simulation for a variety of penetration rates of equipped vehicles.