Slow-moving Vehicles Research Articles

Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement

Eco-driving control techniques have shown significant potential in reducing energy consumption in urban scenarios. The presence of slow-moving vehicles typically disrupts ecological velocity planning, leading to an increase in energy consumption. To solve it, this study proposes a hierarchical eco-driving control strategy, that integrates speed optimization and lane change decision-making in urban scenarios, to not only ensure traffic efficiency, but also save the energy consumption. Firstly, a data-driven energy model is leveraged in the upper level to estimate the energy consumption of candidate lanes and generate ecological lane change decisions. Then, in the lower level, the preceding vehicles and traffic lights are considered to plan an ecological velocity profile via deep reinforcement learning algorithm after transitions to the target driving lane, thereby enhancing the fuel economy and travel efficiency. A virtual driving environment model is established to verify the proposed method through numerous simulation cases. The results indicate that the proposed method effectively reduces energy consumption while maintaining favorable travel efficiency, compared with conventional benchmarks. Furthermore, the notable improvements are observed particularly in free traffic conditions.

Driving towards Sustainability: Wireless Charging of Low-Speed Vehicles with PDM-Based Active Bridge Rectifiers

The surge in demand for eco-friendly transportation and electric vehicle (EV) charging infrastructure necessitates innovative solutions. This study proposed a novel approach to charging slow-moving vehicles, prioritizing efficiency and minimizing output pulsation. Central to the research is the development of a receiver-side power-regulated constant charging system, focusing on power regulation and maintaining consistent charging parameters. This system integrates a receiver-side pulse density-modulated active bridge rectifier, dynamically adjusting driving pulse density to regulate delivered power. Additionally, a receiver-side reconfigurable compensation network ensures constant current and voltage delivery to the charging device, eliminating the need for an additional D.C.-D.C. converter. A 3.3 kW charging structure employing a multi-leg inverter topology and energizing four ground-side transmitter pads exemplifies the proposed approach. The vertical air gap of charging pads is 150 mm, and the system achieves a maximal efficiency of 93.4%. This innovative strategy holds significant promise for advancing sustainable transportation infrastructure and meeting the evolving demands of the EV market.

Risk assessment of rear-end crashes by incorporating vehicular heterogeneity into Bayesian hierarchical extreme value models

Extreme value theory (EVT) has been extensively used to assess road safety with traffic conflicts. However, most studies used pooled models that do not account for vehicle heterogeneity which is characterised by different static and dynamic vehicle parameters such as size, speed, acceleration, and braking capacity. This study proposes a risk assessment technique for rear-end crashes while incorporating vehicular heterogeneity. Video-based trajectory data were collected at four uncontrolled intersections, and conflicts were estimated using modified time-to-collision (MTTC). The crash risk was derived from the observed conflicts using pooled as well as Bayesian hierarchical EVT models. Unlike the pooled model, the hierarchical model revealed that crash risk varies across leader-follower pairs. Interactions that involve cars or light commercial vehicles with slow-moving vehicles are riskier. This study highlights the importance of incorporating vehicular heterogeneity in crash risk assessment. The proposed methodology can be utilised for more accurate risk assessment in heterogeneous traffic.

The Analysis of Service Level on Diponegoro Road, Pasar Aceh: Comparing The Existing Condition and with on-Street Parking Restriction

Traffic congestion is a major problem in cities, especially on the main roads in the Central Business District (CBD) area, as occurred on Diponegoro Road in Banda Aceh, Indonesia. This problem has economic, social, and environmental impacts, such as fuel wastage, longer travel times, and air pollution from vehicle exhaust gases. Factors causing traffic congestion include side friction, such as road crossings (Pedestrian/PED), parking and stop vehicles (PSV), entry and exit vehicles (EEV), and slowmoving vehicles (SMV). Diponegoro Road is a three-lane one-way road (3/1 TT). However, only one lane is effective during peak hours, while the other is used for vehicle parking. Therefore, this study aims to analyze the service level of Dipenogoro Road under existing conditions and with the implementation of on-street parking restrictions. The road performance was calculated using the Indonesian Road Capacity Guidelines (PKJI) 2023 method. The data needed are traffic volume, road geometrics, side friction, and population. The study results show that the road's service level on weekdays (Monday and Thursday) is on level C, indicating that traffic conditions limit the speed and movement of vehicles, and the drivers are restricted in choosing the desired speed. On Sunday mornings, the road level of service is on level B, while in the afternoon, it becomes level D. This difference in service level occurs because there is no activity to access schools or workplaces on Sunday mornings. On Sunday afternoons, most citizens go shopping or sightseeing, causing volume and roadside friction to increase and road performance to drop. The level of road service with the implementation of on-street parking restrictions shows a good level (A and B), with an indication of stable traffic flow, and drivers are free to choose their driving speed. Thus, this research recommends maximizing off-street parking to limit on-street parking.

Modelling Yielding of Slow-Moving Vehicles: Application of Drone Data

Overtaking on two-lane undivided highways poses complex challenges, with attention traditionally focused on the overtaking vehicle. Yet, slow-moving vehicle (impeder) behaviour has been largely overlooked. This study investigates the yielding process whereby impeders actively support overtaking vehicles. This phenomenon can have implications for safety in autonomous environments and optimising Traffic control strategies. Empirical data, including drone-captured trajectories, were analyzed to develop predictive models for yielding behaviour. Prediction accuracies up to 82% and 85% for speed and space yielding were achieved, respectively. Integrating these models into microsimulation tools can supplement policy decisions, optimize Traffic flow, and mitigate accidents, yielding significant societal benefits.

Unscrambling traffic congestion and increasing sustainability in special urban intersection

Traffic congestion in India's densely populated urban centres is a major challenge exacerbated by rapid urbanization and a surge in vehicles surpassing infrastructure growth. Gridlocks and slow-moving vehicles are common due to inadequate road networks and poorly planned intersections, leading to longer commutes, productivity loss, and heightened pollution and safety concerns. Mitigation efforts include infrastructure upgrades and sustainable transportation promotion, implementing Indian Road Congress (IRC) signal design guidelines for intersection optimization and traffic flow management. Pedestrian safety remains critical, requiring comprehensive strategies integrating public transportation enhancement, road infrastructure optimization, and alternative travel modes promotion. Utilizing advanced technology for traffic monitoring, alongside urban planning initiatives prioritizing efficient land use and mixed-use developments, is crucial. Furthermore, policies promoting telecommuting and flexible work schedules can help alleviate the burden of peak-hour traffic. Flyovers offer relief by allowing vehicles to bypass congested areas, improving traffic flow. Congestion indirectly contributes to pollution, emphasizing the importance of catalytic converters in reducing harmful emissions from vehicle exhaust. This comprehensive approach aims to create smoother and more sustainable transportation systems while addressing congestion and pollution issues effectively.

Observations of Surface Gravity Wave Spectra from Moving Platforms

Abstract Surface waves play an important role in the ocean–atmosphere coupled climate system by mediating the exchange of momentum, heat, and gas between the atmosphere and the ocean. Pseudo-Lagrangian autonomous platforms (e.g., Boeing Liquid Robotics Wave Gliders) have been used to investigate the underlying physical dynamics involved in these processes to better parameterize the air–sea exchange occurring at the scale of the surface waves. This requires accurate measurements of directional surface waves down to short scales [O(1) m], as these shorter waves support most of the stress between the atmosphere and the ocean. A challenge to overcome for pseudo-Lagrangian autonomous vehicles is that the platform’s velocity causes the observed frequency of the waves to be Doppler shifted. This leads to a modulation of the wave spectrum, particularly at high frequencies, that depends on the platform’s speed, the wave frequency, and the relative angle between the direction of wave and platform propagation. In this work, we propose a method to account for Doppler effects that considers the full directionality of the wave field. The method is validated using a unique dataset collected from a fleet of two Wave Gliders off the coast of Southern California in September 2019 operating on the perimeter of a tight square (500-m edge length) track over a 3-day deployment. This technique can be used to estimate wave spectra derived from other slow-moving surface vehicles such as Saildrones that use platform motion to characterize the surface wave field. MATLAB routines to implement this method are publicly available. Significance Statement The purpose of this study is to introduce a general approach that corrects observations of ocean surface waves collected on board autonomous surface vehicles (ASVs) for the effects on the wave period due to the vehicle’s forward motion. This is important because improving climate models requires accurate measurements of short-wavelength waves, which can be readily obtained from ASVs. Our method provides the tools for ASVs to better understand air–sea physics and the larger role ocean surface waves play in Earth’s climate system.

The Impact of Road Side Friction on the Traffic Flow of Arterial Roads in Varanasi

Varanasi's prominence as a cultural and historical center means it receives visitors from all over the world. The city's tourism industry is a boon to the local economy. Better transportation infrastructure is crucial to attracting more tourists and increasing revenue. Varanasi is currently experiencing terrible difficulties due to various roadside frictional activities. Vehicle ownership grows in tandem with population growth. The increase in the number of vehicles on the road significantly impacts the reliability of the transportation network since land availability is fixed. The various roadside frictional activities usually found in the streets of Varanasi are on-street parking, pedestrian crossing, and Non-Motorized Vehicles (NMVs). There are not enough legal on-street parking and segregated lanes for NMVs/slow-moving vehicles or demarcation for pedestrian movements in the old city of Varanasi. Vehicles in Varanasi are traditionally parked on the street due to the narrow carriageways. Slow-moving vehicles are forced to move with fast-moving vehicles, and pedestrian crossings affect traffic flow. Integrated movement of slow-moving vehicles and rapid-moving vehicles affects traffic speed, pedestrian crossings impact the Level of Service (LOS), and on-street parking results in the reduction of the effective carriageway width and, hence, road capacity. This paper aims to identify the impact of pedestrian crossings, NMVs, and on-street parking on the traffic flow, speed, capacity, and LOS of urban arterial roads in Varanasi. To achieve this objective, two case studies were considered: the base section (with minimum side friction) and the friction section (with maximum side friction). The videography method was used for data collection. Nine hours of data were collected from 9:30 am to 6:30 pm. The video was played on the screen for data extraction. The speed model was developed by using fundamental diagram methods. Speed-density curve was drawn using Greenshield’s model. The speed-flow curve was derived from the speed-density curve to estimate the capacity at the base and friction sections. Reduction in capacity was determined by comparing friction section capacity with base section capacity. V/C ratio of a particular road compared with the V/C value provided by IRC 106 to predict LOS. A correlation model was developed between the percentage reduction in capacity and road width. Increase in parking, the proportion of NMVs, and pedestrian crossing frequency reduce the traffic flow. It was observed that a 6.42% capacity loss occurs at a 7.00% reduction in effective width, and a 35.09% loss occurs at a 36.02% reduction in effective width. Roads with frictional activities found their LOS class affected up to LOS-E (for the Bhelupur section).

Effectiveness of Climbing Lanes for Slow-Moving Vehicles When Riding Uphill: A Microsimulation Study

Long uphill stretches of single-carriageway rural roads with one lane per travel direction may reduce the Level of Service (LoS), due to the decreased speed of heavy vehicles. In those circumstances, a slowdown of traffic, resulting in the formation of platoons, may be generated due to the difficulty of performing overtaking maneuvers safely. To solve this critical issue, an additional (climbing) lane for slow vehicles may be included in the road platform. This study aims to evaluate the effectiveness of such climbing lanes in a real case in Italy (National Road n. 4 “Via Salaria”—around 44+000 km). Using a microsimulation model implemented in VISSIM, the study analyzes speeds and travel times, delays, and queuing waiting times, comparing the Actual Scenario (AS) without climbing lanes, with two counterfactual scenarios: the first one (CS1) with three stretches of climbing lanes, and the second one (CS2), with just two stretches, in which the first two additional lanes of CS1 are merged together. The obtained results confirm the effectiveness of installing climbing lanes on road sections with the described characteristics, and the potential of microsimulation models also to carry out such kind of evaluations.

Effect of Road Darkness on Young Driver Behaviour when Approaching Parked or Slow-moving Vehicles in Malaysia

Traffic accidents at night are higher than in other periods, especially on a dark road. This study explores the impact of the dark road on driver behaviours when approaching a parked or slowed-moving vehicle at the front. An experiment was conducted on a racing track at night, with and without road lighting. Different sizes of obstacles were used to represent other vehicles as a lorry, a car, and a motorcycle. The results show that the obstacles can be detected by drivers much earlier with the help of road lighting, and they tend to increase their speed, probably preparing to overtake the obstacles.

Solar-powered five-leg inverter-driven quasi-dynamic charging for a slow-moving vehicle

Road transport is becoming increasingly electric as it becomes more environmentally friendly. A green transportation system includes solar arrays along the roadside, encouraging the eco-friendly EV charging system. This paper proposes a road-side photovoltaic system to charge the batteries of slow-moving electric vehicles using a five-leg inverter. The five-legged inverter, which utilizes a stand-alone PV system to drive the charging pads, enhances the probability of achieving the sustainability goal. The limitations of the conventional H-bridge inverter, such as its more prominent design and higher number of switches or straightforward design and restricted power level, are addressed by this converter. The proposed 3.3 kW, 85 kHz inverter energizes the four transmitter pads while a receiver pad moves over the transmitter pads and inductively extracts the power. The d.c.-d.c. converter is used to feed the power to the proposed inverter. The P and O-based MPPT algorithm with a tuned PI controller is used to generate the driving pulses of the d.c.-d.c. converter. The signals are generated based on the voltage and current output of the solar panel output. This control algorithm ensures the stability of the system output response. Additionally, the tuned d.c.-d.c. converter achieves maximum efficiency independent of the load resistance. The system maintains constant power transfer profile concerning load resistance variations. The 520*520 mm Double D-pad transmits the power, while the series-series compensation network assists the charging pads in achieving resonance. The developed systems’ nominal charging voltage and current are 144 V, 20 A, with an equivalent battery resistance of 7.2 Ω.

Data-Driven-Based Eco Approach for Connected and Automated Articulated Trucks in the Space Domain

Since conventional eco approach systems can only achieve longitudinal automation, they may be disabled due to the impedance of the slow-moving vehicle. In addition, they could sacrifice a lot in travel time and it is hard to control the articulated truck with a complex dynamic model. This research presents an enhanced eco approach system for connected and automated articulated trucks, and is able to: (i) overtake slow-moving vehicles for sustainability and mobility; (ii) efficiently optimize the travel duration approaching a signalized intersection; (iii) achieve the trade-off between fuel saving and vehicle mobility; and (iv) improve computational efficiency and optimality for the articulated truck control. To achieve these features, the problem was formulated as an optimal control problem. A longitudinal and lateral coupled truck dynamic model was utilized for enabling the truck to own the automatic overtaking capability. The data-driven-based Koopman operator theory was adopted to globally linearize the truck dynamic model for reducing the computational burden while ensuring optimality. The optimal control problem is transformed from the time domain to the space domain in order for optimizing travel duration and considering the signal timing constraint. A quantitative evaluation was conducted to validate the performance of the Koopman system dynamics. In addition, the simulation experiment was designed to compare the proposed controller against human drivers and the conventional eco approach, which only has longitudinal automation. The results demonstrate that the proposed controller improves the fuel efficiency by 5.12–67.15%, and outperforms the two baseline controllers by 9.09–32.65% in terms of fuel saving. This range is caused by the different arrival times of the ego articulated truck.

Analyzing the effect of fixed and moving bottlenecks on traffic flow and car accidents in a two-lane cellular automaton model

Traffic bottleneck is considered as one of the major causes of the disturbance in traffic flow. The understanding the dynamics between vehicles and bottlenecks is crucial for enhancing traffic flow and ensuring road safety. This research examines a two-lane traffic cellular automaton model to understand the effects of static (e.g., lane reductions) and dynamic (e.g., slow-moving vehicles) bottlenecks on traffic flow and road safety. We found that at low vehicle densities, slow vehicles gravitate towards the open lane, while faster vehicles switch lanes to overtake, returning to their original lane post-bottleneck. At high densities, traffic flow near static bottlenecks ceases, independent of bottleneck length. Safety analysis shows that extended static bottlenecks reduce rear-end collision risk due to fewer lane changes and increased vehicle stationarity. At maximum density, gridlock nullifies the chance of such collisions. Our findings provide actionable insights for traffic planning focused on bottleneck management to improve road safety.

Calibration and evaluation of higher order continuum model for non- lane based mixed traffic environment

ABSTRACT .The present study aims to calibrate a novel multi-class non-lane-based continuum traffic flow model by applying advanced global search optimization algorithms such as Hybrid Search (Combination of genetic algorithm and Nelder Mead) and Generalized Pattern Search. To represent vehicular behavior in non-lane-based mixed traffic environment, the new continuum model is deduced from two-sided lateral gap car following theory along with few empirical observations. The newness of the selected model is that it mimics complex overtaking behavior of vehicles, effect of slow-moving vehicles on traffic stream and driver’s anticipation behavior. The search algorithms considered in this study have been tested and compared using the real-world data. From the analysis, it is observed that the proposed algorithms are more accurate in minimizing the cost function whereas convergence speed is also found to be better. The calibrated parameters are validated using platoon dispersion characteristics of vehicles and alternative transport policy measures.

Statistical physics of the development of Kerner's synchronized-to-free-flow instability at a moving bottleneck in vehicular traffic.

With the use of simulations of a stochastic microscopic traffic model in the framework of the three-phase traffic theory, we have revealed the statistical physics of a traffic flow instability with respect to a transition from synchronized flow (S) to free flow (F) (Kerner's S→F instability) at a moving bottleneck (MB) occurring through a slow-moving vehicle in vehicular traffic. We have found that the S→F instability can occur at the MB more frequently than at an on-ramp bottleneck. From a comparison of the occurrence of the S→F instability at the MB and on-ramp bottleneck at the same probability of traffic breakdown and the same flow rate it has been found that, whereas the frequency of the S→F instability at the on-ramp bottleneck barely changes, the larger the velocity of the MB, the more frequently the S→F instability occurs at the MB. Contrarily, when the MB velocity decreases considerably, then rather than the S→F instability, in synchronized flow at the MB the classical traffic flow instability leading to the emergence of wide-moving jams (S→J instability) occurs. It has been found that the physics of the intensification of the S→F instability at the MB with the increase in the MB velocity is associated with the increase in the mean space gap (mean time headway) between vehicles in synchronized flow. For this reason, when the MB velocity increases, there is an MB velocity at which the S→F instability dominates the S→J instability: The MB velocity influences considerably on the competition between the S→F and classical traffic flow instabilities in synchronized flow.

Navigation and control development for a four-wheel-steered mobile orchard robot using model-based design

Autonomous agricultural robots play an essential role in agricultural mechanization. Self-reliable navigation of these robots is a challenging task. Generally, Global Navigation Satellite Systems (GNSS) based approach, combined with other sensors, is mainly utilized for robot navigation. However, these navigation methods encounter trouble during GNSS signal outages and require a stable navigation solution. This paper proposes a stable navigational algorithm for an orchard robot based on the model-based design that works in a GNSS denied environment. The designed model takes the input driving coordinates and vehicle state from wheel and steering encoders and translates them into vehicle navigational command. The navigation model is built in MATLAB/Simulink environment. The dynamic behavior of the model is neglected, considering that the robot is a slow-moving vehicle due to its application requirement. For robot navigation, the control command given by the controller is divided into velocity and steering control that follows a defined path at a given velocity and steering rate. The developed system is verified both in real and simulation environments. The control algorithm is verified using a mobile robot (non-holonomic) in an outdoor environment. It is confirmed that the robot is able to navigate satisfactorily in the given environment with normalized root mean square difference error within the range of 0.2–0.4 for steering offset at turning and 0.05 degrees during straight travel. Similarly, the model correlation lies close to 0.99 compared to the steering input angle. The proposed navigational model gives a realistic approach for vehicle navigation with the least sensor interaction.

Do Drivers Respond to Lead Vehicles Gradually or Suddenly?

The aim of the current study was to determine whether drivers’ responses to stopped and slow-moving lead vehicles transition gradually in phases, each associated with a distinct optical expansion rate. We also examined whether results were affected by a cell phone conversation and expectancy. We used a driving simulator to assess six response inputs of (1) begins to release the accelerator, (2) releases accelerator completely, (3) starts to press brake (4) comfort-level braking (5) unanticipated-level braking and (6) brake pedal pressed more than 90 percent. Optical expansion rate differed among the first four inputs (the fifth and sixth did not occur enough to analyze), meaning drivers respond to lead vehicles in phases. When drivers were not engaged in a cell phone conversation, optical expansion rate for the perceptual event preceding unanticipated braking was greater when the lead vehicle was stopped compared to slowed. Expectancy did not affect optical expansion rates.

Study of Overtaking Maneuvers on Wide One-Way Roads in Weak Lane-Disciplined Traffic Using Naturalistic Driving Data

Drivers make assumptions and predictions on the expected paths of nearby vehicles. In traffic with weak lane discipline, prediction of the paths of other vehicles becomes more uncertain than in lane-based traffic because of frequent lateral wander. The situation becomes even more challenging on one-way roads where the scope for lateral wander is greater than on two-way roads. Study of overtaking maneuvers on wide one-way roads in the absence of lane discipline therefore becomes imperative. This paper investigates driver behavior while overtaking on wide one-way roads in disorderly traffic using naturalistic driving data collected by an instrumented vehicle. This study proposes definitions of the start and end of the maneuver that lead to behaviorally identifiable time points. The behavior of the overtaking vehicle driver is studied in both longitudinal and lateral directions. Data from 181 overtaking maneuvers on one-way roads of two different road widths are used to study the behavior. The primary observation of the present study is that, although the initial motivation for overtaking is a driver’s sense of urgency because the faster driver of the overtaking vehicle wants to avoid the slow-moving vehicle ahead, once the maneuver starts, the actions of the driver are guided by their concern for safety. One of the major implications of the present study is that the results can be used to develop microsimulation models to analyze driver behavior while overtaking in disorderly traffic streams.

An Enhanced Cell Transmission Model for Multi-Class Signal Control

Existing multi-class cell transmission model (CTM) based methodologies for signal timing or traffic assignment may transfer prioritized transit vehicles from one cell to the next one before processing their preceding passenger cars. In addition, existing CTM-based methodologies process a proportion of a slow-moving transit vehicle in each time step. As such a portion of each transit vehicle remains in each cell and it never clears them. This paper presents constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the CTM. The resulting optimization program is a mixed-integer nonlinear problem. We used a distributed receding horizon control framework to solve it in real-time. The proposed formulation is executed in a simulated arterial street with four signalized intersections in Springfield, IL with different traffic volume levels and transit vehicle frequencies. The results showed that the proposed algorithm addressed the mentioned issues of the existing multi-class CTM, and yielded more efficient network performance than the conventional transit signal priority-based (CTSP) systems. The proposed formulation reduced average bus delay by 1% to 70% and car delay by 52% to 76% compared to CTSP.

Performance Analysis of Deep Learning YOLO models for South Asian Regional Vehicle Recognition

For years, humans have pondered the possibility of combining human and machine intelligence. The purpose of this research is to recognize vehicles from media and while there are multiple models associated with this, models that can detect vehicles commonly used in developing countries like Bangladesh, India, etc. are scarce. Our focus was to assimilate the largest dataset of vehicles exclusive to South Asia in addition to the more common universal vehicles and apply it to track and recognize these vehicles, even in motion. To develop this, we increased the class variations and quantity of the data and used multiple variations of the YOLOv5 model. We trained different versions of the model with our dataset to properly measure the degree of accuracy between the models in detecting the more unique vehicles. If vehicle detection and tracking are adopted and implemented in live traffic camera feeds, the information can be used to create smart traffic systems that can regulate congestion and routing by identifying and separating fast and slow-moving vehicles on the road. The comparison between the three different YOLOv5 models led to an analysis that indicates that the large variant of the YOLOv5 architecture outperforms the rest.