Regular Vehicles Research Articles

Data-Driven Relationship for Reduction Factor of Ballasted Railway Bridge Deflections Due to Load Distribution Within Track

Increasing the operating speed of the trains on modern networks necessitates performing dynamic analyses to assess the performance of bridges under passage of trains. The detailed investigation of their responses requires constructing complex computational models capable to take the train–track–bridge interaction effects into account. Such models have successfully been developed; however, employing those elaborated models for practical engineering applications, or to perform studies that require a large number of analyses may become infeasible. Among such situations are conducting probabilistic investigations, screening of entire networks, or sensitivity analyses. These concerns have been addressed by employing simplified models mostly relying on moving load modeling strategy which disregards the train–track–bridge interaction effects. Those neglected contributions can be compensated by implementing additional correction factors. The distribution of loads within track is one of those disregarded effects where a reduction factor is recommended by design guidelines to take its contribution into account. It has been shown that the existing relationship for these reduction factors delivers an acceptable performance for vertical accelerations, while showing a less favorable performance for displacements. Then, a data-driven strategy is adopted in this study to propose easy-to-apply relationships for reduction factors of deflections, due to load distribution within the track. In this context, three different distributive lengths of triangular load footprints have been considered, namely 2.0, 2.5 and 3.0[Formula: see text]m. The procedure employed has trained and tested for more than 1[Formula: see text]200 train configurations, comprising conventional, articulated and regular vehicles, and including several tens of thousand data points for each distributive length. The performance observed in the new models revealed a considerable improvement with respect to the existing relationship.

Is It Necessarily Better for More Commuters to Share a Vehicle?

Increasing private car ownership has congested urban roads and made parking more difficult, especially during the morning commute. Carpooling offers a new way to address these problems. This paper studies the dynamic departure patterns for both regular and carpooling vehicles with parking space constraints in the morning commute without the carpool lane. The results suggest that as the parking fee of the central cluster increases, the earliest time for the two types of vehicles to enter the central cluster is delayed. The increase in the proportion of regular vehicles delays the earliest time for carpooling vehicles to enter the central cluster. More commuters sharing a vehicle in the morning commute is not necessarily better. Only a reasonable level of carpooling can reduce the peak time and unnecessary time consumption on the road and effectively promote the reduction in parking fees, commuters’ travel costs, and other societal transportation costs. This research gives practical guidance and suggestions on formulating a reasonable parking fee and controlling a reasonable carpooling level.

Electric vehicle (EV) type choice model: Latent class modelling (LCM) approach

This study develops a flexible Latent Class Model (LCM) to investigate the electric vehicle (EV) type choice decisions of Halifax residents. It utilizes cross-sectional data from the 2022 Halifax Travel Activity (HaliTRAC) survey, which includes questions related to EV adoption. This study also analyzes eight attitude and lifestyle preference related statements using principal component analysis (PCA) technique and finally extracts three components labeled as “EV enthusiasts”, “Sustainable travellers”, and “Remote work arrangement admirers”. This paper explores the heterogeneity between two classes for different alternative vehicle type choice, such as, battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), hybrid electric vehicle (HEV), and regular internal combustion engine (ICE) vehicle. Based on class membership attributes, class-1 can be identified as those who live in suburban areas, have a large family with high vehicle ownership, and are interested in travelling with their family members, especially with their children and vice-versa for class-2. Results suggest that variables across two classes portray heterogeneity, such as, full-time worker portray positive correlation for class-1 and negative to class-2; high annual household income group (more than $200,000) exhibit high propensity to choose BEV in class-2 and vice-versa for class-1. Sustainable travelers emphasize the adverse connection towards regular vehicles, while EV enthusiasts demonstrate a favorable association with embracing any type of EV (BEV, PHEV, HEV). Furthermore, the findings from this analysis provide guidance for policy measures such as offering purchase incentives, expanding charging infrastructure, and implementing tax rebates to promote the uptake of EV among the residents of Halifax.

Collaborative optimization of intersection signals and speed guidance for buses run on overlapping route segments under connected environment

AbstractIn order to reduce bus bunching in overlapping route segments and improve the efficiency of bus operation, a dynamic scheduling model is proposed to adjust bus operation states by adopting a cooperative strategy involving multi‐line bus timetable optimization, arterial signal control, and speed guidance. Based on mixed integer linear programming, an arterial signal coordination model with autonomous public transport vehicles (APTVs) dedicated lanes is developed, which enables APTVs to pass through intersections without stopping under conditions that almost have no effect on regular vehicles (RVs). Based on this, a speed guidance strategy of APTVs under connected environment is proposed. After guiding APTVs into the overlapping route segments at a reasonable interval, the optimization goal of maintaining the independent running headway of each bus line to the maximum extent is realized. The simulation verification based on three actual overlapping lines in Hangzhou shows that only the combination of signal coordination considering the characteristics of APTVs and speed guidance can realize the full benefits of bus operation based on dedicated APTVs lane.

Dynamic Modeling and Control of a 4-Wheel Narrow Tilting Vehicle

The automotive industries currently face challenges such as emission limits, traffic congestion, and limited parking, which have prompted shifts in consumer preferences and modern passenger vehicle requirements towards compact vehicles. However, given the inherent limited width of compact vehicles, the potential risk of vehicle rollover is greater than that of regular vehicles. This paper addresses the safety concerns associated with vehicle rollover, focusing on narrow tilting vehicles (NTVs). Quantifying stability involves numerical indicators such as the lateral load transfer ratio (LTR). Additionally, a unique approach is taken by applying ZMP (zero moment point), commonly used in the robotics field, as an indicator of vehicle stability. Effective roll control requires a detailed analysis of the vehicle’s characteristic model and the derivation of lateral and roll dynamics. The paper presents the detailed roll dynamics of an NTV with a MacPherson strut-type suspension. A stability-enhancing method is proposed using a cascade structure based on the internal robust position controller and outer roll stability controller, addressing challenges posed by disturbances. Experimental verification using Simscape Multibody and CarSim validates the dynamic model and controller’s effectiveness, ensuring the reliability of the proposed tilting control for NTVs in practical scenarios.

Energy-optimal car-following model for connected automated vehicles considering traffic flow stability

To reduce energy consumption and transportation emissions during car-following behavior on highways, we propose a car-following model for connected automated vehicles (CAVs). This new model considers spacing variation of the surrounding vehicles located immediately upstream and downstream. We optimize the key parameter of forward control weight of the proposed CAV model to stabilize traffic flow and ensure stable conditions. Then simulation experiments are conducted to validate the effectiveness of our proposed CAV model in enhancing traffic flow stability and energy-saving in mixed traffic consisting of both CAVs and regular vehicles (RVs). Results show that traffic flow controlled by the proposed CAV model is less affected by disturbances. The increase of CAV penetration rates can gradually improve stability of the mixed traffic flow. CAVs equipped with our proposed model can effectively reduce energy consumption and transportation emissions, which decrease with an increase of CAV penetration rates under constant speed conditions. When CAV penetration rate reaches 100 %, the average reduction of energy consumption, CO2 emissions, and NOx emissions at various speeds reach a peak value of 22.15 %, 31.00 %, and 56.41 %, respectively. Speed also has significant influence on energy consumption and emissions, with potential reductions when speed falls within an appropriate range.

Integrating variable speed limit and ramp metering to enhance vehicle group safety and efficiency in a mixed traffic environment

The integrated variable speed limits and ramp metering (VSL-RM) strategy is a useful method to avoid crashes and alleviate congestion on expressways. Previous studies have mostly focused on specific roadway segments and optimized them with the single goal of efficiency or safety, which does not allow for a prompt response to high-risk moving vehicle groups to improve safety and efficiency. To reduce the crash and congestion risk of vehicle groups in real time, this study developed three VSL-RM strategies with different optimization objectives based on predicted risks in a mixed traffic flow environment including connected vehicles (CVs) and regular vehicles (RVs). Due to the different behaviors of CVs and RVs under the VSL-RM control strategy, a mixed traffic METANET model was introduced to predict traffic flow parameters, e.g., volume and speed. Furthermore, two risk prediction models were utilized to predict crash risk and congestion risk based on the traffic flow parameters predicted by the mixed-traffic METANET model. The objectives of the three VSL-RM strategies were to minimize the crash risk, congestion risk, and both crash and congestion risks of the vehicle groups, respectively. These strategies were evaluated using a well-calibrated micro-simulation network. The mixed flow METANET model and the risk prediction model were validated to be consistent with the simulated traffic flow. The results demonstrated that the three strategies could simultaneously improve safety and efficiency benefits in most scenarios. However, the safety-targeted strategy provided the highest safety benefits, while the efficiency-targeted strategy provided the highest efficiency benefits. The bi-objective strategy outperformed the other two strategies in balancing the benefits of safety and efficiency. Moreover, increasing the CV penetration rate resulted in higher benefits for all three strategies.

Enhancing stability of traffic flow mixed with connected automated vehicles via enabling partial regular vehicles with vehicle-to-vehicle communication function

Stability analysis of mixed traffic flow consisting of both connected automated vehicles (CAVs) and regular vehicles (RVs) remains a challenging problem, particularly during the early stage of CAV penetration rates due to stability deterioration caused by the absence of vehicle-to-vehicle (V2V) communication function for CAVs following RVs without this V2V function. This paper presents an analytical approach to stability analysis of the mixed CAV-RV traffic flow by enabling partial RVs with V2V communications to provide connectivity function for CAVs upstream. The proposed analytical approach can be applied to regulate upgrade of RVs equipped with V2V function to mitigate stability deterioration and achieve stable conditions. To begin with, we define this new mixed CAV-RV traffic flow by considering partial RVs with V2V function. We proceed to derive general stability criteria of the mixed traffic flow, based on which the regulated upgrade of RVs equipped with V2V function can be analytically calculated to enhance mixed flow stability at varying CAV penetration rates. Finally, we conduct a numerical experiment to validate the effectiveness of the proposed analytical approach. As the result indicated, both CAV penetration rate and proportion of RVs with V2V function have significant impacts on stability of mixed traffic flow. The study underscores the importance of developing V2V communication connectivity features in a synchronized manner for both CAVs and RVs. By regulating the upgrade of RVs equipped with V2V function according to each CAV penetration rate, stability of the mixed traffic flow can be significantly enhanced.

Optimization strategy for connected automated vehicles to reduce energy consumption on freeway in rainy weather

Energy consumption on freeway significantly contributes to environmental pollution. Rainy weather, as a common adverse condition, will exert a negative impact on car-following behavior of vehicles on freeway and further affect their energy consumption. The emergence of connected automated vehicles (CAVs) has created an opportunity to mitigate these impacts. This paper aims to propose an optimization strategy for CAVs that can reduce energy consumption during car-following behavior on freeway under different rainy weather conditions. To begin with, a calibrated car-following model for regular vehicles (RVs) on freeway in rainy weather was used to derive an optimization strategy for CAVs that have vehicle-to-vehicle communication capability to stabilize traffic flow with smoothed speed fluctuations. The proposed optimization strategy for CAVs was then subjected to simulation experiments to validate its effectiveness. Results indicate that energy consumption on freeway in rainy weather is closely linked to speed fluctuations. Frequent speed fluctuations during car-following behavior could cause more energy consumption. The proposed optimization strategy for CAVs is capable of reducing energy consumption in rainy weather by smoothing speed fluctuations. CAVs equipped with this optimization strategy shows an energy-saving of 34.69%–61.11% compared to RVs under various rainy weather conditions.

Modeling the mixed traffic capacity of minor roads at a priority intersection

As technology for autonomous driving continues to advance, a mixed traffic flow comprising of autonomous vehicles (AVs) and regular vehicles (RVs) is gradually taking shape. Priority intersection is an important type of intersection that connects road networks for traffic efficiency. Essentially, vehicles on major road are given the right of way at these priority intersections, while vehicles on minor road must wait for a safe gap to proceed. Thus, the operation of minor road traffic is a crucial factor that can limit the efficiency of priority intersections. This effect becomes even more complex and diverse in mixed traffic flow consisting of both AVs and RVs. This paper proposes a capacity model for the mixed AVs-RVs traffic flow on minor road at priority intersections. To begin with, we mathematically derive control strategies for homogeneous platoons of AVs or RVs as well as heterogeneous platoons of both AVs and RVs with a specific number of AVs on minor road at priority intersection. Following the framework of the control strategies above, the maximum capacity of mixed traffic on minor road can be optimized under each penetration rate of AVs. Furthermore, we take into account headway distribution on major road in the aforementioned control strategies to complete the capacity modeling approach. We then conduct numerical experiments to evaluate the effectiveness of the proposed capacity model in the mixed traffic. Results show that the proposed model can be applied to quantitatively calculate the mixed traffic capacity at priority intersections, and several important factors have significant impacts on the capacity, such as penetration rate of AVs, traffic volume on major road, and headways of both AVs and RVs on minor road. As the penetration rate of AVs increases, it has a positive impact on the capacity of mixed flow on minor roads. However, this effect is offset by negative impacts of traffic volume on major road and the headway of both AVs and RVs. More specifically, when comparing pure AVs traffic to pure RVs traffic on minor roads, we observe a considerable improvement in capacity ranging from 115.82% to 120.89% under various conditions.

Optimal lane allocation strategy for shared autonomous vehicles mixed with regular vehicles

Autonomous driving technology has the potential to alter the way we travel and is rapidly evolving. Sharing rides in autonomous vehicles may become a popular mode of public transportation in the future. One strategy to enhance operational efficiency is to deploy designated autonomous vehicle (AV) lanes for shared autonomous vehicles (SAVs). This study investigated optimal allocation for dedicated AV lanes considering mixed SAVs and regular vehicles (RVs) flows. First, the standard bottleneck model for morning rush hour was extended to account for both ridesharing and automation, and user equilibrium (UE) solutions were obtained by assuming dedicated AV lanes co-exist with general-purpose (GP) lanes. The equilibrium capacity allocation for dedicated AV lanes was determined for multi-modal traffic scenarios. The study found that commuters may prefer to use SAVs because SAVs were more cost-effective and time-saving. Furthermore, analysis of the fixed payment for ridesharing in SAVs revealed that the system performance could be negatively impacted by high fixed payments. The study proposed an enumeration algorithm for developing lane strategies for discrete lane settings. It was found that the deployment of dedicated AV lanes should be matched to the penetration rate of SAVs and different management objectives, following a gradual rather than a radical process. Finally, a sensitivity analysis was performed considering the value of travel time for SAVs and the bottleneck capacity of a dedicated AV lane. This work provides a new solution to AV lane capacity allocation and offers theoretical support for managers to deploy dedicated AV lane allocation.

An optimal lane configuration management scheme for a mixed traffic freeway with connected vehicle platoons

With the emergent connectivity, both Connected Automated Vehicles (CAVs) and Connected Regular Vehicles (CRVs) have the capability to form platoons that can be defined as connected vehicle platoons, thereby enhancing the capacity of freeways. To fully leverage the innovations of CAVs and CRVs in improving traffic flow mobility, dedicated lanes with distinct vehicle type authority are proposed. Existing studies suggest that the mobility performance of a road section with different lane authority configurations is significantly influenced by the compatibility between lane configurations and traffic flow characteristics, such as penetration rate of connected vehicles (i.e. CAVs and CRVs) and arrival rate per lane. Motivated by the above research need, this paper proposes an optimal lane configuration management scheme to maximize discharge flow in a two-lane freeway mixed traffic environment. To begin with, four lane configuration strategies with different distinct vehicle type authorities are introduced. Analytical models are provided to mathematically derive the discharge flow of given four lane configuration strategies, according to the investigation of single-lane capacity and upstream arriving traffic demand. By configuration strategy-based analytical models, the optimal lane configuration management scheme is proposed. We then conduct numerical analysis to validate the effectiveness of the proposed analytical models of lane configuration strategies under various penetration rates of CAVs and CRVs. As the result indicated, several important factors have significant impacts on the optimal lane configuration management scheme, such as traffic demand and its allocations on each lane, and CAVs/CRVs penetration.

Vehicle seat surface real-time control system with linear actuator to reduce vertical impact

For regular vehicles, the vibration generated when a vehicle passes through a groove or step is dampened but still remains and causes whole-body vibration (WBV) to occupants through vehicle seat. Several epidemiological studies have clearly demonstrated that prolonged exposure to vibration from vehicles is detrimental to human health and may cause motion sickness. To reduce this whole-body vibration. We designed a simple and effective feed-forward control system with compact actuator compatible to the limited under-seat dimension and power outlet of compact general passenger cars and successfully authenticated the possibility of vertical vibration reduction by controlling the vehicle seat surface vertical displacement. In this research, we designed a real-time feed-forward control system with transfer function model estimation. This real-time control system contains a commercial DSP device providing coherence input and output with a controllable delay. With the application of transfer function model and state space model, this system successfully estimated the upcoming vibration acceleration according to the input of the vibrational acceleration time-domain data acquired from lower arm when the test vehicle passing a step. Furthermore, it is possible to process the input data and control the linear actuator precisely to reduce the vertical impact observed as setting force reduction.

Chaotic jam and phase transitions in heterogeneous lattice model integrating the delay characteristics difference with passing effect under autonomous and human-driven vehicles environment

In heterogeneous traffic flow, the response delay of different types' vehicles varies. Accordingly, a heterogeneous lattice model is created for the mixed traffic flows including autonomous vehicles and human-driven vehicles accounting for the delay difference characteristics with the passing effect. The neutral stability condition is acquired from the linear stability analysis, which shows that the stability region of the system significantly decreases with the increase of the passing effect, the proportion of regular vehicles and the reaction time coefficient. The kink-antikink solution of the modified Korteweg-de Vries equation is inferred via nonlinear analysis. The jamming transitions occur between the kink jam and no jam regions with lower passing effect. When the passing effect increases to a critical value, a chaotic jam region appears between the kink jam and no jam regions. And the chaotic jam region further expands with higher passing effect. Moreover, simulation experiments indicate that traffic stability gets better with the delayed time falling down in heterogeneous traffic flow. Also, the impact factors of chaos phenomenon, including the passing effect, the delayed time and proportion of regular vehicles, are investigated in heterogeneous lattice model through Poincaré phase diagram and Lyapunov exponent.

Simultaneous Localization and Mapping and Tag-Based Navigation for Unmanned Aerial Vehicles

This paper presents navigation techniques for an Unmanned Aerial Vehicle (UAV) in a virtual simulation of an indoor environment using Simultaneous Localization and Mapping (SLAM) and April Tag markers to reach a target destination. In many cases, UAVs can access locations that are inaccessible to people or regular vehicles in indoor environments, making them valuable for surveillance purposes. This study employs the Robot Operating System (ROS) to simulate SLAM techniques using LIDAR and GMapping packages for UAV navigation in two different environments. In the Tag-based simulation, the input topic for April Tag in ROS is camera images, and the calibration of position with a tag is done through assigning a message to each ID and its marker image. On the other hand, navigation in SLAM was achieved using a global and local planner algorithm. For localization, an Adaptive Monte-Carlo Localization (AMCL) technique has been used to identify factors contributing to inconsistent mapping results, such as heavy computational load, grid mapping accuracy, and inadequate UAV localization. Furthermore, this study analyzed the April Tag-based navigation algorithm, which showed satisfactory outcomes due to its lighter computing requirements. It can be ascertained that by using ROS packages, the simulation of SLAM and Tag-based UAV navigation inside a building can be achieved.

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Private and public transportation will be dominated by Autonomous Vehicles (AV), which are safer than regular vehicles. However, ensuring good performance for the autonomous features requires fast processing of heavy tasks. Providing each AV with powerful computing resources may result in increased AV cost and decreased driving range. An alternative solution is to install low-power computing hardware on each AV and offload the heavy tasks to powerful nearby edge servers. In this case, the AV’s reaction time depends on how quickly the navigation tasks are completed in the edge server. To reduce task completion latency, the edge servers must be equipped with enough network and computing resources to handle the vehicle demands, which show large spatio-temporal variations. Thus, deploying the same resources in different locations may lead to unnecessary resource over-provisioning. In this paper, we leverage simulations using real traffic data to discuss the implications of deploying heterogeneous resources in different city areas to sustain peak versus average demand of edge-assisted AVs. Our analysis indicates that a reduction in network bandwidth and computing cores of up to 60% and 50%, respectively, is achieved by deploying edge resources for the average demand rather than peak demand. We also investigate how the peak-hour demand affects the safe travel time of AVs and find that it can be reduced by approximately 20% if they would be rerouted to areas with a lower edge-resource load. Thus, future research must consider that traditional turn-by-turn navigation systems may not provide the fastest routes for edge-assisted AVs.

A Study on Impact of Green Marketing Influences on Consumers' Decision to Purchase Electric Vehicles in the Automobile Industry, with Reference to Raipur City

The paper helps us identify the effect of green marketing practices of automobile manufacturing companies on consumers. It is important for the researcher to know how "Green" the consumers are in terms of their knowledge, their attitudes and behavior. This study finds how far advertising that promotes environmental friendliness influences people in Raipur to buy electric Vehicles. Electric vehicles are getting more attention because they're seen as better for the environment than regular vehicles that run on gas. Figuring out what makes people want to buy electric vehicles is important for getting more of them on the roads and dealing with environmental problems. The research adopts a quantitative research design and collects data through a structured questionnaire survey. The target population consists of residents in Raipurcity who are potential consumers of EVs. The study employees a convenience sampling technique to select participants, resulting in a sample size of 120. The research examines the relationship between green marketing strategies adopted by EV manufacturers and consumers intention to purchase EVs. It investigates various green marketing dimensions, such as eco-labelling, eco-packaging, and eco-branding, and their impact on consumer’s perceptions of EVs environmental benefits. The research looks at how the way electric car companies advertise their vehicles being good for the environment affects whether people want to buy them. It can also help companies that make and sell electric vehicles in Raipur. If they know what makes people want to buy electric vehicles, they can make plans to encourage more people to buy them. Companies can also use this information to make advertisements that focus on the good things about electric vehicles for the environment, which might make more people want to buy them.

Regular Vehicle Spatial Distribution Estimation Based on Machine Learning

For the mixed traffic flow, obtaining the distribution of connected vehicles (CVs) and regular vehicles (RVs) is of great significance for road network analysis and cooperative control in intelligent transportation systems (ITSs). However, whether it is based on fixed sensors or based on CVs and traffic mechanism to estimate the spatial distribution of RVs, the implementation complexity and low estimation accuracy are the points that need to be improved. This paper proposes a regular vehicle spatial distribution estimation method using adjacent connected vehicles as mobile sensors. First, to investigate the hidden relationship between the interaction information of adjacent CVs and the spatial distribution of RVs among CVs, the Gaussian mixture model-hidden Markov model (GMM-HMM) is selected as the identification method. Then, three sets of experiments were designed to study the influence of observed features on the identification capability of the model, generalization capability validation, and comparison with other methods, respectively. Finally, the proposed method is verified by the dataset generated by the car-following model. The experimental results show that selecting the relative position and time headway as observed features can effectively reflect the regular vehicle spatial distribution between adjacent CVs. The average accuracy of the proposed method to identify the regular vehicle spatial distribution is over 93.7%, which can provide valuable suggestions for the Internet of Vehicles application.

Travel Demand Increment Due to the Use of Autonomous Vehicles

Advanced technology available in promising fully autonomous vehicles (AVs) will encourage people to travel more than they have up to now using their regular vehicles. It is believed that the disadvantages of manually driven vehicles, such as driving fatigue, stressful feelings, aging physical skills deterioration, and other unwanted effects, will vanish once AVs are employed. For this purpose, this study presents the results of a large questionnaire performed in Győr, Hungary, about the public perception and acceptance of AVs. In addition, assessing the impact of using such technology on increasing travel demand when people can alter their mode of transport to an AV. The study demonstrates that respondents’ prior knowledge of AVs plays a crucial role in generating a greater number of trips when they are able to use AVs. Furthermore, it has also been found that providing further awareness and education to the population about the meaning of the term autonomous vehicles and widening their insights about the new features provided by these vehicles will result in a higher number of trips. Eventually, this information will act as a considerable indicator to provide a prior understanding of the possible challenges that may impact the sustainability of future transport systems.

Ecological Cooperative Adaptive Control of Connected Automate Vehicles in Mixed and Power-Heterogeneous Traffic Flow

The development of vehicle electrification and intelligent network technologies has led to a new type of mixed and power-heterogeneous traffic flow, comprised of regular vehicles (RVs) and connected and automated vehicles (CAVs), fuel vehicles (FVs) and electric vehicles (EVs). To reduce the energy consumption of mixed and power-heterogeneous traffic flow operating at a signalized intersection, the Ecological Control Unit–Cooperative Adaptive Control (ECU-CACC) is proposed in this paper. The vehicle platoon is divided into units which are named minimum ecological control units (min-ECUs). A bi-level control framework is designed to improve traffic efficiency and reduce energy consumption. The lower-level aims to plan the best ecological trajectory for every min-ECU, and the upper-level optimizes the passing strategies for efficiency through speed coordination. Scenario numerical experiments are performed to verify the effectiveness of the bi-level optimal control model and analyze the energy-saving effect of ECU-CACC under different vehicle mixing situations. The results from the experiment prove the excellent energy-saving potential of the proposed ECU-CACC, which helps the min-ECUs save about 10–20% energy consumption compared with a regular pattern.