Uncertain Traffic Conditions Research Articles

Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

Markov game for CV joint adaptive routing in stochastic traffic networks: A scalable learning approach

This study proposes a learning-based approach to tackle the challenge of joint adaptive routing in stochastic traffic networks with Connected Vehicles (CVs). We introduce a Markov Routing Game (MRG) to model the adaptive routing behavior of all vehicles in such networks, thereby incorporating both competitive route choices and real-time decision-making. We establish the existence of the Nash policy (i.e., optimal joint adaptive routing policy) within the MRG that enables vehicles to adapt optimally to real-time traffic conditions online through efficient communication. To enhance scalability, we innovate with a homogeneity-based mean-field approximation method and, based on that, further develop the Homogeneity-based Mean-Field Deep Reinforcement Learning (HMF-DRL) algorithm to learn the Nash policy within the MRG. Through numerical experiments on the Nguyen–Dupuis network, we demonstrate our algorithm’s ability to efficiently converge and learn the joint adaptive routing policy that significantly enhances traffic network efficiency. Furthermore, our study provides insights into the effects of travel demand, penetration of CVs, and levels of uncertainty on the performance of the joint adaptive routing policy. This paper presents a significant step towards improving network efficiency and reducing the travel time for a majority of vehicles amid uncertain traffic conditions.

Eco-driving at signalized intersections under uncertain traffic conditions

Eco-driving at signalized intersections is a topical issue in CAVs technology and an important part of energy-efficient driving. In this paper, a robust eco-driving control method of vehicular platoon is proposed to address the uncertain traffic conditions, including traffic queuing effects and signal time variations. Firstly, an effective signalization model is established by estimating the traffic queue length and describing the uncertain parameters with the interval number. Then, taking fuel economy and vehicle safety as the objectives, the uncertain objective function is transformed into a deterministic optimization problem by using the order interval relation model. Finally, a model predictive control method is used to optimize the problem. The simulation results indicate that the proposed method can significantly improve the control robustness and fuel economy without the signal history data.

Truck–drone hybrid routing problem with time-dependent road travel time

Combining trucks and drones in package delivery provides a promising venue for a future logistics system that is more efficient and sustainable than the existing one. However, how to coordinate trucks and drones, particularly under uncertain traffic conditions (thus, travel time), remains a critical question in this field. To address this challenge, this study proposes and solves a truck–drone hybrid routing problem with time-dependent road travel time (TDHRP-TDRTT) to address the truck–drone cooperation issue. TDHRP-TDRTT is formulated as a cost minimization problem with constraints associated with logistics demand and supply. An iterative local search heuristic algorithm based on intra-pair and inter-pair customer exchanges and link re-optimization is developed to solve TDHRP-TDRTT. Our results on small-scale and benchmark instances show that the proposed algorithm has better computational performance than CPLEX solver, the adaptive large neighborhood search, hybrid genetic-sweep algorithm, and variable neighborhood search. A case study using traffic data from Chongqing, China shows that the truck–drone solution improves the timeliness of delivery, undertakes sensitivity analysis considering four road congestion states, significantly reduces trucking mileage, and facilitates overcoming terrain limitations. Therefore, the proposed model and algorithm are of practical significance in reducing operating cost, improving transportation efficiency, and facilitating a smart and sustainable urban logistics distribution system.

Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon

Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning systems are more versatile to handle uncertain traffic conditions than fully automated platooning systems. This paper presents a novel distributed stochastic model predictive control (DSMPC) design for a human-leading heavy-duty truck platoon. The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon. The driver behavior of the human-driven leader truck is learned by a stochastic inverse reinforcement learning (SIRL) approach. The proposed stochastic driver behavior model aims to learn a distribution of cost function, which represents the richness and uniqueness of human driver behaviors, with a given set of driver-specific demonstrations. The distributed formation control includes a serial DSMPC with guaranteed recursive feasibility, closed-loop chance constraint satisfaction, and string stability. Simulation studies are conducted to investigate the efficacy of the proposed design under several realistic traffic scenarios. Compared to the baseline platoon control strategy (deterministic distributed model predictive control), the proposed DSMPC achieves superior controller performance in constraint violations and spacing errors.

Multistage Robust Optimization of Routing and Scheduling of Mobile Energy Storage in Coupled Transportation and Power Distribution Networks

Mobile energy storage systems (MSSs) manifest a significant potential for enhancing the reliable and economic operations of distribution systems with high photovoltaic (PV) penetrations. This article proposes a robust and dynamic MSS scheduling method, which includes MSS mobility and its power management, in a coupled transportation and power distribution network. A rolling-horizon multistage robust optimization model with integer and linear decisions is proposed to schedule the MSS mobility and its charging and discharging strategies, considering uncertain traffic conditions, PV power output, and load demands, as well as nonanticipativity MSS operation constraints. The proposed model is transformed into a mixed-integer linear programming (MILP) problem to achieve computational tractability based on mixed-linear and binary decision rules, and the duality theory. The proposed dynamic MSS scheduling method is tested on the augmented 33- and 123-bus distribution systems with actual solar irradiance, load, and traffic data; the simulation results are compared with those considering day-ahead MSS scheduling and stationary energy storage systems to verify the effectiveness of the proposed dynamic MSS operation.

Online predictive connected and automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project.

A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions

Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a data-driven optimization-based Model Predictive Control (MPC) modeling framework for the Cooperative Adaptive Cruise Control (CACC) of a string of CAVs under uncertain traffic conditions. The proposed data-driven optimization-based MPC modeling framework aims to improve the stability, robustness, and safety of longitudinal cooperative automated driving involving a string of CAVs under uncertain traffic conditions using Vehicle-to-Vehicle (V2V) data. Based on an online learning-based driving dynamics prediction model, we predict the uncertain driving states of the vehicles preceding the controlled CAVs. With the predicted driving states of the preceding vehicles, we solve a constrained Finite-Horizon Optimal Control problem to predict the uncertain driving states of the controlled CAVs. To obtain the optimal acceleration or deceleration commands for the CAVs under uncertainties, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with a Distributionally Robust Chance Constraint (DRCC). The predicted uncertain driving states of the immediately preceding vehicles and the controlled CAVs will be utilized in the safety constraint and the reference driving states of the DRSO-DRCC model. To solve the minimax program of the DRSO-DRCC model, we reformulate the relaxed dual problem as a Semidefinite Program (SDP) of the original DRSO-DRCC model based on the strong duality theory and the Semidefinite Relaxation technique. In addition, we propose two methods for solving the relaxed SDP problem. We use Next Generation Simulation (NGSIM) data to demonstrate the proposed model in numerical experiments. The experimental results and analyses demonstrate that the proposed model can obtain string-stable, robust, and safe longitudinal cooperative automated driving control of CAVs by proper settings, including the driving-dynamics prediction model, prediction horizon lengths, and time headways. Computational analyses are conducted to validate the efficiency of the proposed methods for solving the DRSO-DRCC model for real-time automated driving applications within proper settings.

Warehouse Vis: A Visual Analytics Approach to Facilitating Warehouse Location Selection for Business Districts

AbstractSelecting a proper warehouse location serving to satisfy the demands of the goods from a certain business area is important to a successful retail business. However, the large solution space, uncertain traffic conditions, and varying business preferences impose great challenges on warehouse location selection. Conventional approaches mainly summarize relevant evaluation criteria and compile them into an analysis report to facilitate rapid data absorption but fail to support a comprehensive and joint decision‐making process in warehouse location selection. In this paper, we propose a visual analytics approach to facilitating warehouse location selection. We first visually centralize relevant information of warehouses and adapts a widely‐used methodology to efficiently rank warehouse candidates. We then design a delivering estimation model based on massive logistics trajectories to resolve the uncertainty issue of traffic conditions of warehouses. Based on these techniques, an interactive framework is proposed to generate and explore the candidate warehouses. We conduct a case study and a within‐subject study with baseline systems to assess the efficacy of our system. Experts ‘feedback also suggests that our approach indeed helps them better tackle the problem of finding an ideal warehouse in the field of retail logistics management.

OPTIMASI PENDISTRIBUSIAN PRODUK MENGGUNAKAN MODEL FUZZY MULTIOBJEKTIF CYCLICAL INVENTORY ROUTING PROBLEM

In distribution activities, travel time is influenced by several factors, such as vehicle conditions, traffic conditions and road conditions. Uncertain traffic conditions cause the travel time can not be defined with certainty (uncertain). If several variables are in a uncertain condition then a deterministic approach is not appropriate. Fuzzy approach, can be used to overcome uncertainty conditions. In this study, the optimal route for chicken egg delivery using the fuzzy Multiobjective Cyclical Inventory Routing Problem (FMOCIRP) model with time and cost is expressed by triangular fuzzy numbers. Model completion using Winqsb software. An optimal shipping route is obtained with a total cost of Rp 114,000 and a total delivery time of 132 minutes. The first optimal delivery route is distributor, retailer 1, retailer 2, retailer 3 and back to the distributor. The second route is distributor, retailer 5, retailer 4 and back to the distributor.

Biobjective Emergency Logistics Scheduling Model Based on Uncertain Traffic Conditions

Emergency logistics scheduling appears more and more important in modern society because of frequent occurrence of unpredictable disasters. Most of the existing studies consider a certain emergency logistics scheduling model, and most of them are based on an ideal scenario. Considering the uncertain traffic condition and the real road condition, a biobjective emergency logistics scheduling model is proposed, which includes two objectives: transportation time and transportation cost. The uncertainty of the proposed model is reflected in two aspects: the occurrence time of emergencies and the traffic volume predicted by the cloud model. The numerical characteristics of traffic information are abstracted from the spatial-temporal trajectory data by the reverse cloud model, and the inference procedure of the one-dimension cloud model further predicts the uncertain traffic volume using the numerical characteristics. In addition, the crossover and mutation operators of multiobjective evolutionary algorithms are modified to solve the model. The experimental results show that the inference procedure of one-dimension cloud model can accurately predict the traffic volume at the departure time; and the proposed model is more reasonable than the existing scheduling models; at the same time, the improved NSGA-II can also provide superior schemes in different departure times and traffic conditions for decision makers.

Tour recommendation and trip planning using location-based social media: a survey

Tourism is both an important industry and popular leisure activity undertaken by millions around the world. One important task for tourists is to plan and schedule tour itineraries that comprise multiple captivating Points-of-Interests based on the unique interest preferences of the tourist. The complex task of tour itinerary recommendation is further complicated by the need to incorporate various real-life constraints such as limited time for touring, uncertain traffic conditions, inclement weather, group travel, queuing times and crowdedness. In this survey, we conduct a comprehensive literature review of studies on tour itinerary recommendation and present a general taxonomy for touring-related research. We discuss the entire process of tour itinerary recommendation research including: (i) data collection and types of datasets; (ii) problem formulations and proposed algorithms/systems for individual travellers, groups of tourists and various real-life considerations; (iii) evaluation methodologies for comparing tour itinerary recommendation algorithms; and (iv) future directions and open problems in tour itinerary recommendation research.

Route and speed optimization for autonomous trucks

Autonomous vehicles, and in particular autonomous trucks (ATs), are an emerging technology that is becoming a reality in the transportation sector. This paper addresses the problem of optimizing the routes and the speeds of ATs making deliveries under uncertain traffic conditions. The aim is to reduce the cost of emissions, fuel consumption and travel times. The traffic conditions are represented by a discrete set of scenarios, using which the problem is modeled in the form of two-stage stochastic programming formulations using two different recourse strategies. The strategies differ in the amount of information available during the decision making process. Computational results show the added value of stochastic modeling over a deterministic approach and the quantified benefits of optimizing speed.

A Multi-Trip Split-Delivery Vehicle Routing Problem with Time Windows for Inventory Replenishment Under Stochastic Travel Times

This study considers a multi-trip split-delivery vehicle routing problem with soft time windows for daily inventory replenishment under stochastic travel times. Considering uncertainty in travel times for vehicle routing problems is beneficial because more robust schedules can be generated and unanticipated consequences can be reduced when schedules are implemented in reality. However, uncertainties in model parameters have rarely been addressed for the problems in this category mainly due to the high problem complexity. In this study, an innovative and practical approach is proposed to consider stochastic travel times in the planning process. In the planning model, the possible outcomes of vehicle arrivals and product delivery at retailers are systematically categorized and their associated penalty and reward are estimated. Thus, unanticipated costs for every scheduling decision can be incorporated into the planning model to generate vehicle routing schedules that are more robust facing uncertain traffic conditions. To solve the model that is characterized as an NP-hard problem in a reasonable amount of time, a two-stage heuristic solution algorithm is proposed. Finally, the stochastic model is compared with the deterministic model in both planning and simulated operation stages using the data of a supply chain in Taiwan. The result confirms that the schedule generated by the stochastic model is more robust than the one created with the deterministic model because undesired outcomes such as unfulfilled demands are greatly reduced.

Sensor Communications towards Intelligent Vehicle Networking

Sensor technologies have been widely used to make transportation clean, efficient, connected, and safe. Roadside wireless sensor and networking technology represents a costeffective solution and can leverage the deployment of the system as a whole. Sensors installed in cars continuously gather important information, such as air bags, distance detection, mechanical and electronic parts, tire pressure, collision force, and passengers’ conditions. Transmission of such information between cars can be used to implement differentiated mobile services. As a consequence, Wireless Vehicular Sensor Network (VSN) has been widely accepted in developing the next generation intelligent transportation application systems as well as connected vehicles networking. However, VSN does not operate like the traditional wireless sensor network (WSN). For example, the motion of the vehicle, the environment of the roads, and other uncertain traffic conditions all pose challenges to the system. For safety reason, the vehicle-vehicle and vehicle-gateway communication modes must be stable. Therefore, how to keep link stability becomes an important issue. This special issue is in response to the increasing convergence of distributed sensor networks. Papers selected for this special issue represent recent progresses in the field, including works on mobility architectures, vehicle communications and networking, algorithms in wireless sensor networks, and services and applications in distributed systems. All of these papers not only provide novel ideas and state-of-the-art techniques in the field but also stimulate future research in the sustainable environment. 2. Architecture and Mobility

Network Optimization for Energy Saving Considering Link Failure with Uncertain Traffic Conditions

Network Optimization for Energy Saving Considering Link Failure with Uncertain Traffic Conditions

A Mean-Risk Model for the Traffic Assignment Problem with Stochastic Travel Times

Heavy and uncertain traffic conditions exacerbate the commuting experience of millions of people across the globe. When planning important trips, commuters typically add an extra buffer to the expected trip duration to ensure on-time arrival. Motivated by this, we propose a new traffic assignment model that takes into account the stochastic nature of travel times. Our model extends the traditional model of Wardrop competition when uncertainty is present in the network. The focus is on strategic risk-averse users who capture the trade-off between travel times and their variability in a mean-standard deviation objective, defined as the mean travel time plus a risk-aversion factor times the standard deviation of travel time along a path. We consider both infinitesimal users, leading to a nonatomic game, and atomic users, leading to a discrete finite game. We establish conditions that characterize an equilibrium traffic assignment and find when it exists. The main challenge is posed by the users' risk aversion, since the mean-standard deviation objective is nonconvex and nonseparable, meaning that a path cannot be split as a sum of edge costs. As a result, even an individual user's subproblem—a stochastic shortest path problem—is a nonconvex optimization problem for which no polynomial time algorithms are known. In turn, the mathematical structure of the traffic assignment model with stochastic travel times is fundamentally different from the deterministic counterpart. In particular, an equilibrium characterization requires exponentially many variables, one for each path in the network, since an edge flow has multiple possible path-flow decompositions that are not equivalent. Because of this, characterizing the equilibrium and the socially optimal assignment, which minimizes the total user cost, is more challenging than in the traditional deterministic setting. Nevertheless, we prove that both can be encoded by a representation with just polynomially many paths. Finally, under the assumption that the standard deviations of travel times are independent from edge loads, we show that the worst-case ratio between the social cost of an equilibrium and that of an optimal solution is not higher than the analogous ratio in the deterministic setting. In other words, uncertainty does not further degrade the system performance in addition to strategic user behavior alone.

A Mean-Risk Model for the Traffic Assignment Problem with Stochastic Travel Times

Heavy and uncertain traffic conditions exacerbate the commuting experience of millions of people across the globe. When planning important trips, commuters typically add an extra buffer to the expected trip duration to ensure on-time arrival. Motivated by this, we propose a new traffic assignment model that takes into account the stochastic nature of travel times. Our model extends the traditional model of Wardrop competition when uncertainty is present in the network. The focus is on strategic risk-averse users who capture the tradeoff between travel times and their variability in a mean-standard deviation (mean-stdev) objective, defined as the mean travel time plus a risk-aversion factor times the standard deviation of travel time along a path. We consider both infinitesimal users, leading to a non-atomic game, and atomic users, leading to a discrete finite game.We establish conditions that characterize an equilibrium traffic assignment and find when it exists. The main challenge is posed by the users’ risk aversion, since the mean-stdev objective is non-convex and non-separable, meaning that a path cannot be split as a sum of edge costs. As a result, even an individual user’s sub-problem — a stochastic shortest path problem — is a non-convex optimization problem for which no polynomial time algorithms are known. In turn, the mathematical structure of the traffic assignment model with stochastic travel times is fundamentally different from the deterministic counterpart. In particular, an equilibrium characterization requires exponentially many variables, one for each path in the network, since an edge-flow has multiple possible path-flow decompositions that are not equivalent. Because of this, characterizing the equilibrium and the socially-optimal assignment, which minimizes the total user cost, is more challenging than in the traditional deterministic setting. Nevertheless, we prove that both can be encoded by a representation with just polynomially-many paths. Finally, for the case of uncertainty parameters that are independent from edge loads, we show that although an equilibrium assignment results in a total user cost that is higher than that of the socially-optimal one, it is not higher than the analogous ratio in the deterministic setting. In other words, uncertainty does not further degrade the system performance in addition to strategic user behavior alone.

Mining Discriminative Pattern to Classify the Quick and Safe Travelling in the Mode of Road Network

Categorization is one of the major tasks in the field of traffic network. In comparison to the other techniques which can be correlated with the categorization tries to find out all the frequent blueprints which existed in the input categorical of the traffic network satisfying the public specified least support or inequity measures like minimum confidence or information gain. Those blueprints are used shortly either as rules for rule-based classifier or training features for Support Vector Machine (SVM) categorizer. Several algorithms have been proposed to solve the categorization problem on uncertain conditions in the road transportation due to improper traffic network. In this research paper we have proposed an innovative algorithm concord which extracts discriminative patterns directly and effectively from the uncertain traffic network conditions as classified in the features which assists everyone to use the road transportation in traveling quick and safe to any destinations.

Efficient Routing of Emergency Vehicles under Uncertain Urban Traffic Conditions

Emergency-vehicle drivers who aim to reach their destinations through the fastest possible routes cannot rely solely on expected average travel times. Instead, the drivers should combine this travel-time information with the characteristics of data variation and then select the best or optimal route. The problem can be formulated on a graph in which the origin point and destination point are given. To each arc in the graph a random variable is assigned, characterized by the expected time to traverse the arc and the variance of that time. The problem is then to minimize the total origin-destination expected time, subject to the constraint that the variance of the travel time does not exceed a given threshold. This paper proposes an exact pseudo-polynomial algorithm and an e-approximation algorithm (so-called FPTAS) for this problem. The model and algorithms were tested using real-life data of travel times under uncertain urban traffic conditions and demonstrated favorable computational results.