Adaptive scheduling for real-time and temporal information services in vehicular networks

Vehicular networks represent a research area of significant importance in improving the safety, efficiency and sustainability of transportation systems. One of the key research problems in vehicular networks is real-time data dissemination, which is crucial to the satisfactory performance of many emergent applications providing real-time information services in vehicular networks. Specifically, the two issues need to be addressed in this problem are maintenance of temporal data freshness and timely dissemination of data. Most existing works only considered periodical data update via backbone wired networks in maintaining temporal data freshness. However, many applications rely on passing vehicles to upload their collected information via wireless network, which imposes new challenges as the uplink data update will have to compete with the downlink data dissemination for the limited wireless bandwidth. With such observations, we propose a temporal information service system, in which vehicles are able to collect up-to-date temporal information and upload them to the roadside units (RSU) along their trajectories. Meanwhile, RSU can disseminate its available data items to vehicles based on their specific requests. Particularly, in this paper, we first quantitatively analyze the freshness of temporal data and propose a mathematical model to evaluate the usefulness of the temporal data. Next, we give the formulation of the proposed real-time and temporal information service (RTIS) problem, and prove the NP-hardness of this problem by constructing a polynomial-time reduction from 0–1 knapsack problem. Subsequently, we establish a probabilistic model to theoretically analyze the tradeoff between timely temporal data update and requested data dissemination sharing a common communication resource, which provides a deeper insight of the proposed RTIS. Further, a heuristic algorithm, namely adaptive update request scheduling (AURS), is designed to enhance the efficacy of RTIS by synthesizing the broadcast effect, the real-time service requirement and the service quality in making scheduling decisions. The computational complexity and scalability analysis of AURS is also discussed. Last but not least, a simulation model is implemented and a comprehensive performance evaluation has been carried out to demonstrate the superiority of ARUS against several state-of-the-art approaches in a variety of application scenarios.     

Robust control of traffic networks under uncertain conditions

Uncertainty of traffic network operations has been a subject of lively debate in the last decade. However, little effort has been put in developing control frameworks that are not only aimed at improving the average performance of the system, but also at improving the system robustness and reliability. In fact, it can be argued that most of the current control approaches are only aimed at improving the efficiency, which can even be counterproductive from a robustness point of view. The main contributions of this article is the proposition of a new control framework based on the notion of controlled Markov processes, which explicitly takes into account the uncertainty in predicted traffic conditions and system performance. Furthermore, in contrast to traditional optimal control approaches, the objective function can include general statistic of the random system performance, such as the mean, standard deviation or 95‐percentile. The contribution aims to make clear how different performance function specifications yield different control strategies. This is shown for a relatively simple case study.     

Optimization models to characterize the broadcast capacity of vehicular ad hoc networks

Broadcast capacity of the entire network is one of the fundamental properties of vehicular ad hoc networks (VANETs). It measures how efficiently the information can be transmitted in the network and usually it is limited by the interference between the concurrent transmissions in the physical layer of the network. This study defines the broadcast capacity of vehicular ad hoc network as the maximum successful concurrent transmissions. In other words, we measure the maximum number of packets which can be transmitted in a VANET simultaneously, which characterizes how fast a new message such as a traffic incident can be transmitted in a VANET. Integer programming (IP) models are first developed to explore the maximum number of successful receiving nodes as well as the maximum number of transmitting nodes in a VANET. The models embed an traffic flow model in the optimization problem. Since IP model cannot be efficiently solved as the network size increases, this study develops a statistical model to predict the network capacity based on the significant parameters in the transportation and communication networks. MITSIMLab is used to generate the necessary traffic flow data. Response surface method and linear regression technologies are applied to build the statistical models. Thus, this paper brings together an array of tools to solve the broadcast capacity problem in VANETs. The proposed methodology provides an efficient approach to estimate the performance of a VANET in real-time, which will impact the efficacy of travel decision making.     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.     

拱桥侧墙病害链式机理及其断链减灾方法

文章根据灾害链式理论,分析了拱桥侧墙病害的链式机理及规律,提出了基于断链减灾思想的加固治理方法。通过工程实践证明,基于链式理论的病害分析与加固治理方法是可行的。     

Coloured Petri net-based traffic collision avoidance system encounter model for the analysis of potential induced collisions

The Traffic Alert and Collision Avoidance System (TCAS) is a world-wide accepted last-resort means of reducing the probability and frequency of mid-air collisions between aircraft. Unfortunately, it is widely known that in congested airspace, the use of the TCAS may actually lead to induced collisions. Therefore, further research regarding TCAS logic is required. In this paper, an encounter model is formalised to identify all of the potential collision scenarios that can be induced by a resolution advisory that was generated previously by the TCAS without considering the downstream consequences in the surrounding traffic. The existing encounter models focus on checking and validating the potential collisions between trajectories of a specific scenario. In contrast, the innovative approach described in this paper concentrates on quantitative analysis of the different induced collision scenarios that could be reached for a given initial trajectory and a rough specification of the surrounding traffic. This approach provides valuable information at the operational level. Furthermore, the proposed encounter model can be used as a test-bed to evaluate future TCAS logic changes to mitigate potential induced collisions in hot spot volumes. In addition, the encounter model is described by means of the coloured Petri net (CPN) formalism. The resulting state space provides a deep understanding of the cause-and-effect relationship that each TCAS action proposed to avoid an actual collision with a potential new collision in the surrounding traffic. Quantitative simulation results are conducted to validate the proposed encounter model, and the resulting collision scenarios are summarised as valuable information for future Air Traffic Management (ATM) systems.     

The Hamilton–Jacobi partial differential equation and the three representations of traffic flow

This paper applies the theory of Hamilton–Jacobi partial differential equations to the case of first-order traffic flow models. The traffic flow surface is analyzed with respect to the three 2-dimensional coordinate systems arising in the space of vehicle number, time and distance. In each case, the solution to the initial and boundary value problems are presented. Explicit solution methods and examples are shown for the triangular flow-density diagram case. This unveils new models and shows how a number of existing models are cast as special cases.     

Stochastic frontier estimation of budgets for Kuhn–Tucker demand systems: Application to activity time-use analysis

We propose a stochastic frontier approach to estimate budgets for the multiple discrete–continuous extreme value (MDCEV) model. The approach is useful when the underlying time and/or money budgets driving a choice situation are unobserved, but the expenditures on the choice alternatives of interest are observed. Several MDCEV applications hitherto used the observed total expenditure on the choice alternatives as the budget to model expenditure allocation among choice alternatives. This does not allow for increases or decreases in the total expenditure due to changes in choice alternative-specific attributes, but only allows a reallocation of the observed total expenditure among different alternatives. The stochastic frontier approach helps address this issue by invoking the notion that consumers operate under latent budgets that can be conceived (and modeled) as the maximum possible expenditure they are willing to incur. The proposed method is applied to analyze the daily out-of-home activity participation and time-use patterns in a survey sample of non-working adults in Florida. First, a stochastic frontier regression is performed on the observed out-of-home activity time expenditure (OH-ATE) to estimate the unobserved out-of-home activity time frontier (OH-ATF). The estimated frontier is interpreted as a subjective limit or maximum possible time individuals can allocate to out-of-home activities and used as the time budget governing out-of-home time-use choices in an MDCEV model. The efficacy of this approach is compared with other approaches for estimating time budgets for the MDCEV model, including: (a) a log-linear regression on the total observed expenditure for out-of-home activities and (b) arbitrarily assumed, constant time budgets for all individuals in the sample. A comparison of predictive accuracy in time-use patterns suggests that the stochastic frontier and log-linear regression approaches perform better than arbitrary assumptions on time budgets. Between the stochastic frontier and log-linear regression approaches, the former results in slightly better predictions of activity participation rates while the latter results in slightly better predictions of activity durations. A comparison of policy simulations demonstrates that the stochastic frontier approach allows for the total out-of-home activity time expenditure to either expand or shrink due to changes in alternative-specific attributes. The log-linear regression approach allows for changes in total time expenditure due to changes in decision-maker attributes, but not due to changes in alternative-specific attributes.