Mixed Vehicle Research Articles

Influence of Large Vehicles on the Speed of Expressway Traffic Flow

Large vehicles impact the quality of traffic flow. To predict the impact of large‐scale vehicles on the average speed of traffic flow, vehicle speeds under different vehicle mixing rates were collected through field observations. A laser roadside traffic survey instrument with automatic vehicle type identification functionality was used to collect cross section traffic flow data. The v/C ratio, large vehicle mixing rate, and average speed of traffic were calculated for each data set. A total of 158 traffic flow data sets were captured and divided into three groups according to the v/C ratio of the expressway. The v/C ratio ranges of the three groups are v/C ≤ 0.35, 0.35 < v/C ≤ 0.55, and 0.55 < v/C ≤ 0.90. SPSS software was used to analyze the correlation between the vehicle mixing rate and the average speed under different traffic flow conditions, and a model was determined between the average speed of the vehicle flow and the large vehicle mixing rate. Analysis of the results with SPSS revealed a negative logarithmic linear relationship between the average traffic speed and the mixing rate of large vehicles. The results could also be applied to passenger cars. The models are considered as corrections of the average speed of the traffic flow after the mixing of large vehicles. When the mixing rate of large vehicles is close to zero, the forecast value of the model is the average speed of passenger cars. Furthermore, as the traffic volume of the road section increased, the influence of the mixing rate on traffic flow speed became more obvious. The adaptability of the proposed prediction model of the expressway mixing rate was verified by evaluating model predictions against actual measurements.

Collaborative Fleet Deployment and Routing for Sustainable Transport

Efficient multi-modal transportation in the hinterland of seaport terminals depends on consolidation of container volumes in support of frequent services of high capacity means of transport, such that sustainable multi-modal transport can compete with uni-modal road transport in cost and time. The tactical design of barge scheduled transport services involves fleet selection and routing through the inland waterway network. The resulting network service design should meet expected demand and service time requirements set by the shippers. We develop a tight MILP formulation for the Fleet Size and Mix Vehicle Routing (FSMVRP) especially adapted for the Port-Hinterland multi-modal barge network design. Also, an analytical model is developed to help understand important design trade-offs made. We consider the case of horizontal cooperation of dry port container terminals that share capacity. Our results show that in case of cooperation, both cost savings and service levels are improved, and allow for sustainable multi-modal transport to be competitive with uni-modal truck transport.

How Will the Presence of Autonomous Vehicles Affect the Equilibrium State of Traffic Networks?

It is known that connected and autonomous vehicles are capable of maintaining shorter headway and distances when they form platoons of vehicles. Thus, such technologies can potentially increase the road capacities of traffic networks. Consequently, it is envisioned that their deployment will also increase the overall network mobility. In this paper, we examine the validity of this expected impact, assuming that drivers select their routes selfishly, in traffic networks with mixed vehicle autonomy, that is, traffic networks with both regular and autonomous vehicles. We consider a nonatomic routing game on a network with inelastic (fixed) demands for a set of network origin destination (O/D) pairs, and study how replacing a fraction of regular vehicles by autonomous vehicles will affect the mobility of the network. Using well-known U.S. Bureau of Public Roads traffic delay models, we show that the resulting Wardrop equilibrium is not necessarily unique for networks with mixed autonomy. Then, we state the conditions under which the total network delay at equilibrium is guaranteed to not increase as the fraction of autonomous vehicles increases. However, we show that when these conditions do not hold, counterintuitive behaviors may occur-the total network delay can grow as the fraction of autonomous cars increases. In particular, we prove that for networks with a single O/D pair, if the road degrees of capacity asymmetry (i.e., the ratio between the road capacity when all vehicles are regular and the road capacity when all vehicles are autonomous) are homogeneous, the total delay is: 1) unique and 2) a nonincreasing continuous function of the fraction of autonomous vehicles in the network. We show that for heterogeneous degrees of capacity asymmetry, the total delay is not unique, and it can further grow as the fraction of autonomous vehicles increases. We demonstrate that similar behaviors may be observed in networks with multiple O/D pairs. We further bound such performance degradations due to the introduction of autonomy in general homogeneous networks.

Integrated Municipal Solid Waste Management under uncertainty: A tri-echelon city logistics and transportation context

The growing challenge of Municipal Solid Waste Management in mega-cities calls for development of practical decision-making support tools to assist authorities in City Logistics and Urban Planning. This study aims to optimize the logistics network and transportation system of ISWM where the complete chain of MSW/residue is formulated as a tri-echelon ISWM logistics network. Assuming various levels of complexity of a realistic ISWM system, a Mixed-Integer Linear Programming (MILP) model is developed to formulate the ISWM system in the framework of the Fleet Size and Mix Vehicle Routing Problem with Time Windows. Addressing uncertainty in ratios of MSW generation, a two-stage stochastic optimization approach is proposed to effectively support the cost-effective ISWM transportation system by determining the optimal fleet size and decomposition, vehicles routes and capacity-allocation to the system components. The proposed approach successfully was applied to a real case of ISWM in southern Tehran, Iran. The numerical experiments showed the usefulness of the approach to minimize the economic costs of the system under uncertainty. Furthermore, the results verified the strength and effectiveness of the method when experiencing larger deviations between the estimated and actual amounts of the uncertain parameter and in cases of unplanned disruptions in the system network.

Variable fleet size and mix VRP with fleet heterogeneity in Integrated Solid Waste Management

The Integrated Solid Waste Management (ISWM) is a recent effective tool to manage with the growing challenge of Municipal Solid Waste (MSW). The ISWM integrates all the system components (i.e. transfer, treatment, recycling and disposal of wastes) to enhance the sustainable waste management whilst reducing operational costs. In this paper, we investigate an integrated framework of the Fleet Size and Mix Vehicle Routing Problem (VRP) to optimize the cost-effective ISWM system. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is developed to concurrently minimize the transportation cost in the entire waste management system and total deviation from the fair load allocation to transfer stations. A complete ISWM system with all interdependent facilities and multiple technologies, is developed to address a tri-echelon Fleet Size and Mix VRP with a heterogeneous fleet of vehicles under multiple technologies and waste compatibility constraints. The model was solved for both the Preemptive and Non-Preemptive conditions using Lexicographic and Goal Programming optimization approaches. The model was tested on a case of ISWM in the Southern part of Tehran, Iran.

Size-resolved particle emission factors of vehicular traffic derived from urban eddy covariance measurements

Road traffic is one of the major sources of atmospheric particles in urban areas. Modelling the contribution of this source to urban pollution requires reliable estimates of emission factors possibly resolved in size. In this work, size-resolved particle emission factors (EFs) of the mixed vehicle fleet were measured in the urban area of Lecce (south-eastern Italy). The approach used is based on vertical fluxes measured with eddy-covariance, counting of vehicles, and estimation of footprints. Results show that the average EF in number (range 0.009–3 μm) was 2.2*1014 #/Veh km, being dominated by ultrafine particles (Dp < 0.25 μm) due to exhaust emissions. EF number size distribution decreases with particle size. A reduction of more than four orders of magnitude was observed at Dp ≅ 0.9 μm. EF mass size distribution reaches a maximum around Dp ≅ 0.3 μm then decreases until Dp ≅ 0.9 μm. For larger particles EF in mass increases for the influence of non-exhaust emissions. Average emission factor of PM1 was 56 mg/Veh Km and that of PM2.5 was 63 mg/Veh Km. A comparison of measurements taken in 2010 and 2015 in the same area shows a decreasing trend of the average total EF in number of about 56%, likely as a consequence of the increased use of new generation vehicles following more restrictive limits for particle emissions.

Analysis of traffic flow with micro-cars with respect to safety and environmental impact

This study analyzes the characteristics of traffic flow in the presence of micro-cars in a vehicle mix. A two-lane multi-cell traffic cellular automaton (TCA) model is formulated to simulate mixed traffic flow comprising conventional passenger cars and micro-cars. The segments of an urban highway and arterial road, both with two lanes and measuring 700 m in length, are simulated; the latter includes an intersection delay with a signal cycle at the midpoint. Traffic flows with different proportions of micro-cars are investigated in the simulation. Four aspects of traffic conditions are calculated: the number of lane changes, the number of decelerations, the coefficient of speed variation (which may be indicative of safety performance), and the HC, CO, and NOx emissions (as a measure of environmental impact). The simulation results suggest that mixed flow with micro-cars leads to higher frequencies of lane-changing on both highways and arterial roads, although the incremental change on the latter is smaller. With the introduction of micro-cars on the highway, the frequency of decelerations increases in free flow and decreases in congested flow; however, on the arterial road, it decreases the frequency of decelerations and has an insignificant impact on free flow. The introduction of micro-cars has a similar impact on the coefficient of speed variation. Under free flow conditions on highways, the introduction of micro-cars has a negative influence on the three aforementioned parameters related to safety. However, for free flow on arterial roads or congested flow on highways and arterial roads, the results are inconclusive because the effect of micro-cars is contradictory in terms of the three parameters. Vehicle emissions, such as HC, CO, and NOx, increase during free flow on highways, but are always lower on arterial roads.

Application of a variable neighborhood search algorithm to a fleet size and mix vehicle routing problem with electric modular vehicles

The emergence of new promising technologies used in electric transportation offers opportunities to achieve sustainable urban freight deliveries. In particular fleets of electric vehicles are seen as an efficient ecologic and economic transportation alternative for operating last-mile goods distribution in cities. This paper deals with the use of electric modules which can be added or removed from a freight vehicle to help gain capacity or respect delays when delivering customers. Since the electric vehicles have a limited battery capacity, they have to stop and recharge them at dedicated stations during the delivery tours. However, with the modular feature, this problem of disturbing the whole distribution process for recharging the batteries can be reduced and sometimes overlooked. This paper objective is to design and find the optimal routing strategy for a fleet of electric modular vehicles operating the last-mile delivery, with the minimal cost including the acquisition, the travel and the recharging ones. The research problem arising from the structure of the fleet can be associated with both a truck and trailer problem and a fleet size and mix vehicle routing problem with time windows using electric vehicles. This problem is presented in the first part of the article with the proposed solving procedure. More precisely, for big instances of the problem which is NP-Hard, a Variable Neighborhood Search (VNS) is designed and adapted. Experimental studies carried out on some benchmark instances show that the VNS performs impressively well in comparison with the best solving approaches of the literature.

Improving autonomous vehicle safety—Using acoustic source localization to influence the decision making capabilities of an autonomous vehicle

The manner in which autonomous vehicles (AVs) and other vehicles will coexist, and communicate with one another, is still unclear, especially during the prolonged period of mixed vehicles sharing the road. During this time, it will be important that AVs are equipped to respond to acoustic alerts in urban environments. Consider the case of an emergency vehicle in a city. In this situation, a visual perception system alone will not suffice, as it requires a direct view of the source. The goal of this ongoing project is to define a new set of audio-visual detection and localization tools to identify the location of an unexpected rapidly approaching emergency vehicle. In particular, this paper focuses on the localization of a sound source in a complex environment by combining a ray tracing approach with a direction-of-arrival algorithm. This algorithm reports a number of source directions, arising from multiple reflections in the environment. Results are combined with a three-dimensional map, acquired live from a mobile robot equipped with digital sensors including LiDAR, and a reverse ray tracing approach is used to triangulate the likely position of the source.

Improving autonomous vehicle safety—The use of convolutional neural networks for the detection of warning sounds

In cities across the world everyday, people use and process acoustic alerts to safely interact in and amongst traffic. With the advent of autonomous vehicles (AVs), the manner in which these new vehicles can use these acoustic cues to supplement their decision making process is unclear. This will be especially important during the prolonged period of mixed vehicles sharing the road. One solution may lie in the advancement of machine learning techniques; it has become possible to teach a machine (or a vehicle) to recognize certain sounds. This paper reports on an ongoing project with the objective of identifying emergency vehicles sirens in traffic and alerting the vehicle to take rapid evasive action. In particular, we report on the use of a deep layer Convolutional Neural Network (CNN) trained to recognize emergency sirens. We retrained a CNN (AlexNet) to recognize sirens in real time. To utilize this network, samples from the ESC-50 dataset for environmental sound classification were processed and each converted to a spectrogram. This CNN can be used in conjunction with a microphone array to accurately recognize sirens in traffic and identify the direction from which the emergency vehicle is approaching.

An energy-efficient green-vehicle routing problem with mixed vehicle fleet, partial battery recharging and time windows

We investigate a specific version of the Green Vehicle Routing Problem, in which we assume the availability of a mixed vehicle fleet composed of electrical and conventional (internal combustion engine) vehicles. These are typically light- and medium-duty vehicles. We allow partial battery recharging at any of the available stations. In addition, we use a comprehensive energy consumption model which can take into account speed, acceleration, deceleration, load cargo and gradients. We propose a matheuristic embedded within a large neighborhood search scheme. In a numerical study we evaluate the performance of the proposed approach.

The Effect of Nano-Alumina Particles Generated &amp;lt;i&amp;gt;in Situ&amp;lt;/i&amp;gt; in the Matrix of Ultralow and No Cement 85 wt% Al&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; Refractory Castables

Advanced high alumina refractory castables of ultra-low and to cement types, are well-known because of their ability on developing similar and/or superior thermal and mechanical properties. Following the recent trend of including nanoparticles in refractory castables, in this work, it is presented a novel way to obtain the benefit effects on the thermal and mechanical properties, promoted by the development in situ, of alumina’s nanoparticles in the matrix of castable (85 wt% Al2O3). The alumina nanoparticles were originated in situ after firing, due to the pyrolysis and oxidation of an aqueous resin, produced by the Pechini process. The resin played a double role, one as mixing liquid vehicle and the other as the aluminum oxide nanoparticles precursor. The results indicate a strong increase in flexural strength and elastic modulus as well as leading to a higher residual strength after thermal shock.

A memetic algorithm for fleet size and mix vehicle routing problems with electric modular vehicles

This work deals with an extension of the well-known vehicle routing problem with time windows (VRPTW), where the fleet consists of electric modular vehicles (EMVs). The main drawback of managing electric vehicles is that they have a limited range. Here the vehicles are modular which means that payload modules are carried by a cabin module and can be detached at certain customer locations allowing the rest of the vehicle to continue the tour. This can also permit to recharge the battery of some modules to further capitalise on the gained energy. To tackle the resulting research problem, a comprehensive mathematical formulation is proposed to take into account the multiple constraints linked with the modularity, the electric charging, time windows to serve the customers and capacity issues. Due to the NP-hardness of the problem, a memetic algorithm is implemented and tested for designing good quality solutions in reasonable computational times. Extensive computational experiments carried out on some benchmark instances show the effectiveness of both the problem formulation and the memetic algorithm.

Distribution of driving force beneath wheeled vehicle with varying center of gravity

Driving force analysis is performed on the no-spin differential and full-time all-wheel-drive vehicle; this thesis takes an automatic loading mixing vehicle as an example to introduce the compositions and working principle of the driving system. Based on the tire-ground mechanics, the model of the dynamics and the kinematics is established under the walking straight and steering conditions. According to the theoretical model, the influence of the vehicle’s gravity center on the moving system is analyzed. Co-simulation based on LMS Imagine Lab AMESim and LMS Virtual Lab Motion is performed to build the hydraulic driving system and the multi-body dynamics system models. Based on the tire-ground load environment simulation model built by 1D + 3D, various positions of the gravity center of the model are set to compare with the theoretical analysis. Various weight blocks are also added to change the location of the gravity center in the practical experiment. The conclusions that different gravity center positions lead to the change of the driving torque distribution are proved by the simulation results and experimental data.

A memetic algorithm for fleet size and mix vehicle routing problems with electric modular vehicles

A memetic algorithm for fleet size and mix vehicle routing problems with electric modular vehicles

Chemical Composition of Particulate Matter from Traffic Emissions in a Road Tunnel in Xi’an, China

ABSTRACT Chemical compositions of particulate matter (PM) from traffic emissions vary by region and with time. Therefore, it is necessary to obtain local mobile source profiles of PM to support regional researches for vehicle emission control policy, source apportionment modeling, etc. In this study, PM2.5 and PM10 samples were collected from a highway tunnel in Xi’an in northwestern China. The chemical composition, specifically, the OC, EC, water-soluble ions, and elements, was analyzed in detail to (1) provide local PM profiles for a mixed vehicle fleet, (2) identify the origins of different elements in the tunnel environment, and (3) determine the associated factors influencing the profiles. The PM2.5 profiles in the tunnel were identified as OC (34.10%), EC (11.96%), water-soluble ions (18.22%), and elements (27.73%), while the PM10 profiles included OC (28.48%), EC (8.59%), water-soluble ions (14.17%), and elements (33.36%), respectively. The origins of the elements in the tunnel were classified into three categories by the receptor modeling approach: resuspended road dust and brake wear, vehicle exhaust and tire wear, and tailpipe emissions from diesel vehicles (DV). The mass fractions of OC, EC, and elements originating from resuspended road dust and brake wear were mainly affected by vehicle driving conditions (i.e., uphill/downhill and speed), whereas the mass content of bromine (Br) was influenced by the proportion of DV in the fleet.

FarSight

Maintaining an adequate separation from the vehicle in front is key to safe driving. While LIDAR and RADAR sensors could be used for ranging, cost considerations and the huge installed base of vehicles that lack these sensors, especially in developing regions, call for a low-cost yet robust alternative. To this end, we present FarSight, a system that performs vehicle ranging using a smartphone mounted on the windshield or the dashboard. FarSight uses the smartphone's camera to identify and draw a bounding box around vehicles in front, based on which ranging is performed. Unlike prior smartphone-based work, FarSight does not depend on any infrastructure support such as standard-width lane markers and works with a heterogeneous mix of vehicles, both of which are characteristics of developing regions. We develop a novel hybrid approach for vehicle detection and tracking, which balances accuracy and speed by combining deep neural network based vehicle detection with vision-based object tracking in a pipelined manner. We also devise data augmentation techniques to improve the effectiveness of vehicle detection, thereby increasing the ranging distance. We show that FarSight can range accurately in both daytime and nighttime conditions and up to distances of 90 m. We have implemented FarSight as an Android-app and tested it across various phones. Further, we present two ranging-based applications built on FarSight.

A Model for Multiple Traveling Salesman Problem with Mixed Type Vehicles

As carbon emissions increase, environmental awareness is getting stronger and electric bicycles are being used in the field of logistics. Developed countries such as Europe are implementing eco-friendly logistics by introducing electric bicycles, but in some countries, they are operated on trial basis due to various geographical constraints. This study deals with the problem of routing plan between the two types of vehicles that occurs when the electric bicycle is partially introduced into the existing truck delivery system. The proposed mixed type vehicle mTSP is an extension of the commonly known mTSP in which many identical vehicles deliver to all demand points and return to depot while in mixed type vehicle mTSP, two different kinds of transport take over the same area and deliver. Between two specific demand points, there is a bypath that only electric bicycles can travel at a lower cost, and the route that trucks can’t move. This study discusses the problem of overlapping delivery routes when providing delivery service for the same area with two different characteristics as described above and aims at finding an integrated routing for an efficient route.

Efficient and Collision-Free Anticipative Cruise Control in Randomly Mixed Strings

Connected intelligent vehicle following can improve safety and efficiency compared to today's road transport, but real traffic in the near future will not provide an ideal setting for its deployment. Unconnected human-driven vehicles will follow variable behavior patterns that combine with differences in dynamic capability to create heterogeneous scenarios. In this paper, connected automated vehicles employ model predictive control for following in traffic that may include both heavy and passenger vehicles at quasi-random positions. Adding further realism, some quasi-randomly mixed vehicles are completely unconnected and equipped with the reactive intelligent driver model using pseudorandom parameters. A mixed-integer quadratic programming formulation adapts the predictive algorithm to diverse powertrain operating point constraints. The preceding vehicle's control input is estimated from velocity and brake light observations and used to probabilistically generate a preview for the ego vehicle. A terminal constraint designed using particle kinematics prevents collisions due to shortsightedness. Results are simulated at various heavy and predictive vehicle concentrations in the presence of packet loss. Linear fuel economy improvements between 1.4% and 1.9% per 10% increase in predictive vehicle penetration rate are shown.

Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors

Any vehicle needs to be aware of its localization, destination, and neighboring vehicles’ state information for collision free navigation. A centralized controller computes controls for cooperative adaptive cruise control (CACC) vehicles based on the assumed behavior of manually driven vehicles (MDVs) in a mixed vehicle scenario. The assumed behavior of the MDVs may be different from the actual behavior, which gives rise to a model mismatch. The use of erroneous localization information can generate erroneous controls. The presence of a model mismatch and the use of erroneous controls could potentially result into collisions. A controller robust to issues such as localization errors and model mismatches is thus required. This paper proposes a robust model predictive controller, which accounts for localization errors and mitigates model mismatches. Future control values computed by the centralized controller are shared with CACC vehicles and are stored in a buffer. Due to large localization errors or model mismatches when control computations are infeasible, control values from the buffer are used. Simulation results show that the proposed robust controller with buffer can avoid almost the same number of collisions in a scenario impacted by localization errors as that in a scenario with no localization errors despite model mismatch.