Evaluate the sustainable marketing strategy to optimal online leasing of new energy vehicles under the background big data economy

PurposeUnder the big data background, there are many influencing factors for investors of new energy vehicles (NEV), and government subsidies promote the sustainable development of the new energy vehicle industry. Therefore, the purpose of the study is to provide solutions for the sustainable development of NEV.Design/methodology/approachThe sustainable marketing strategy of NEV in China is put forward. This paper first analyzes the subsidy policy effect of NEV under the background of big data. It then establishes the online optimal leasing strategy under multiple strategy choices and the online leasing strategy of multiple vehicles under the inflation market.FindingsWith the fixed cost of NEV in each lease period, the optimal competition ratio of online decision-makers will continue to decrease with the increase of the difference between prepaid funds and government subsidies. In the decision-making of renting and purchasing multiple vehicles, the general strategy competition ratio is 2.922, while the optimal competition ratio of the online renting and purchasing strategy proposed by the research is 2.723.Research limitations/implicationsThe research is limited by the limited data and information collected, so the optimal decision-making model has some limitations. The authors need to find more representative data to optimize the model.Practical implicationsAs an emerging industry, NEV have developed rapidly in recent years. Based on the online algorithm and competitive ratio theory, this paper solves the decision-making problem of operators and gives the optimal strategy to promote the green development of the new energy vehicle industry.Originality/valueThis paper proposes the optimal strategy for online investors of new energy vehicle operators by combining online algorithm and competitive ratio theory. The numerical analysis results of the optimal online model under multi strategy selection show that with the same difference between prepaid funds and government subsidies, the time point will be delayed and the time point will be advanced as the cost of leasing NEV in each period increases.

Optimization Model of Transit Route Fleet Size Considering Multi Vehicle Type

This paper proposes a bus line capacity optimization design model considering the scale of multiple vehicles, which is achieved by minimizing system operating costs and user costs. The proposed model takes into account the difference of passenger demand in different periods, and can get the optimal headway and delivery and reserve plan. In order to prove that the method can effectively minimize the cost, we solved a numerical example and compared the cost of the method in multi-transit model planning. Furthermore, the optimization results show that the total costs (TC) were reduced by 14.48%. Among them, the user costs (UC) decreased by 30.38% and the operator costs (OC) increased by 4.18%. Sensitivity analyses are presented to verify the validity of the model. The analysis results show that multi size bus optimization can reduce the total cost, especially the user cost in a certain cost weight interval. Besides this, the cost weight which reflects the passenger volume and waiting time value, optional bus size and cross-section passenger volume all affect vehicle scheme and system cost.

Controlling the Connected Vehicle with Bi-Directional Information: Improved Car-Following Models and Stability Analysis

Connected vehicle (CV) technologies are changing the form of traditional traffic models. In the CV driving environment, abundant and accurate information is available to vehicles, promoting the development of control strategies and models. Under these circumstances, this paper proposes a bidirectional vehicles information structure (BDVIS) by making use of the acceleration information of one preceding vehicle and one following vehicle to improve the car-following models. Then, we deduced the derived multiple vehicles information structure (DMVIS), including historical movement information of multiple vehicles, without the acceleration information. Next, the paper embeds the four kinds of basic car-following models into the framework to investigate the stability condition of two structures under the small perturbation of traffic flow and explored traffic response properties with different proportions of forward-looking or backward-looking terms. Under the open boundary condition, simulations on a single lane are conducted to validate the theoretical analysis. The results indicated that BDVIS and the DMVIS perform better than the original car-following model in improving the traffic flow stability, but that they have their own advantages for differently positioned vehicles in the platoon. Moreover, increasing the proportions of the preceding and following vehicles presents a benefit to stability, but if traffic is stable, an increase in any of the parameters would extend the influence time, which reveals that neither β1 or β2 is the biggest the best for the traffic.

Reliability-Based Multi-Vehicle Path Planning Under Uncertainty Using a Bio-Inspired Approach

Identifying a reliable path in uncertain environments is essential for designing reliable off-road autonomous ground vehicles (AGV) considering post-design operations. This paper presents a novel bio-inspired approach for model-based multi-vehicle mission planning under uncertainty for off-road AGVs subjected to mobility reliability constraints in dynamic environments. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility (i.e., maximum attainable speed) for any given terrain and soil conditions. Based on physics-based simulations, the vehicle state mobility reliability in operation is then analyzed using an adaptive surrogate modeling method to overcome the computational challenges in mobility reliability analysis by adaptively constructing a surrogate. Subsequently, a bio-inspired approach called Physarum-based algorithm is used in conjunction with a navigation mesh to identify an optimal path satisfying a specific mobility reliability requirement. The developed Physarum-based framework is applied to reliability-based path planning for both a single-vehicle and multiple-vehicle scenarios. A case study is used to demonstrate the efficacy of the proposed methods and algorithms. The results show that the proposed framework can effectively identify optimal paths for both scenarios of a single and multiple vehicles. The required computational time is less than the widely used Dijkstra-based method.

Modeling of Electrified Transportation Systems Featuring Multiple Vehicles and Complex Power Supply Layout

The paper proposes a novel approach to modeling electrified transportation systems. The proposed solution reflects the mechanical dynamics of vehicles as well as the distribution and losses of electric supply. Moreover, energy conversion losses between the mechanical and electrical subsystems and their bilateral influences are included. Such a complete model makes it possible to replicate, e.g., the impact of voltage drops on vehicle acceleration or the necessity of partial disposal of regenerative braking energy due to temporary lack of power transmission capability. The modeling methodology uses a flexible twin data-bus structure, which poses no limitation on the number of vehicles and enables modeling complex traction power supply structures. The proposed solution is suitable for various electrified transportation systems including suburban and urban systems. The modeling methodology is applicable i.a. to Matlab/Simulink, which makes it broadly available and customizable, and provides short computation time. The applicability and accuracy of the method were verified by comparing simulation and measurement results on an exemplary trolleybus system operating in Pilsen, Czech Republic. Simulation of daily operation of an area including four supply sections and maximal simultaneous number of nine vehicles showed a good conformance with the measured data, with the difference in the total consumed energy not exceeding 5%.

Integrating bridge influence surface and computer vision for bridge weigh-in-motion in complicated traffic scenarios

Complicated traffic scenarios, including random change of vehicles’ speed and lane, as well as the simultaneous presence of multiple vehicles on bridge, are main obstacles that prevents bridge weigh-in-motion (BWIM) technique from reliable and accurate application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method which integrates deep-learning-based computer vision technique and bridge influence surface theory. In this study, bridge strains and traffic videos are recorded synchronously as the data source of BWIM. The computer vision technique is employed to detect and track vehicles and corresponding axles from traffic videos so that spatio-temporal paths of vehicle loads on the bridge can be obtained. Then a novel method is proposed to identify the strain influence surface (SIS) of the bridge structure based on the time-synchronized strain signals and vehicle paths. After the SIS is identified, the axle weight (AW) and gross vehicle weight (GVW) can be identified by integrating the SIS, time-synchronized bridge strain, and vehicle paths. For illustration and verification, the proposed method is applied to identify AW and GVW in scale model experiments, in which the vehicle-bridge system is designed with high fidelity, and various complicated traffic scenarios are simulated. Results confirm that the proposed method contributes to improve the existing BWIM technique with respect to complicated traffic scenarios.

Stochastic Analysis on the Resonance of Railway Trains Moving over a Series of Simply Supported Bridges

Resonance problems encountered in vehicle-bridge interaction (VBI) have attracted widespread concern over the past decades. Due to system random characteristics, the prediction of resonant speeds and responses will become more complicated. To this end, this study presents stochastic analysis on the resonance of railway trains moving over a series of simply supported bridges with consideration of the randomness of system parameters. A train-slab track-bridge (TSB) vertically coupled dynamics model is established following the basic principle of vehicle-track-coupled dynamics. The railway train is composed of multiple vehicles, and each of them is built by seven rigid parts assigned with a total of 10 degrees of freedom. The rail, track slab, and bridge are considered as Euler–Bernoulli beams, and the vibration equations of which are established by the modal superposition method (MSM). Except for the nonlinear wheel-rail interaction based on the Hertz contact theory, the other coupling relations between each subsystem are assumed to be linear elastic. The number theory method is employed to obtain the representative sample point sets of the random parameters, and the flow trajectories of probabilities for the TSB dynamics system are captured by a probability density evolution method (PDEM). Numerical results indicate that the maximum bridge and vehicle responses are mainly dominated by the primary train-induced resonant speed; the last vehicle of a train will be more seriously excited when the bridges are set in resonance by the train; the resonant speeds and responses are rather sensitive to the system randomness, and the possible maximum amplitudes predicted by the PDEM are significantly underestimated by the traditional deterministic method; optimized parameters of the TSB system are preliminary obtained based on the representative point sets and imposed screening conditions.

TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4

Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

Research on effects and influence by presenting information on priority order and oncoming vehicle to operators for teleoperation of multiple autonomous vehicles

With self-driving vehicles, it is possible to manage multiple vehicles from a remote location even if one observer does not have a driver in the driver’s seat. Therefore, demonstration experiments are being conducted in various places to remotely monitor two autonomous vehicles and operate them as needed. However, when one observer manages multiple vehicles, the amount of information that can be processed is limited. If we can assist with an appropriate amount of information, we may be able to manage more vehicles. In this study, we perform an experiment in which the priority and the type of assist information are changed and presented to the observer in the overtaking scene of a parked vehicle using a simulator. Focusing on the burden on the observer during remote management of multiple units, the purpose is to identify the information required for monitoring and reduce the burden from changes depending on the type of information to be assisted.

Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements

Multiple-target tracking algorithms generally operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field-of-view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field-of-view. To address this problem, we propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves and then re-enters the UAV’s field-of-view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, receding horizon and deep reinforcement learning, and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. In addition, we develop an algorithm that computes the upper bound on the filter’s performance, thus facilitating an estimate of the number of UAVs needed to achieve a desired performance threshold.