交叉口有交通信号控制时用户最优动态配流模型

针对智能交通系统（ＴＴＳ）基础上中主要基础理论之一的动态交通分配问题,在现有研究成果的基础上,提出了更接近真实路网的多起点多旋点交叉口设置有交通信号控制（Ｔｒａｆｆｉｃ Ｓｉｇｎａｌ Ｃｏｎｔｒｏｌ）时的动态配流模型,给出了对现有的通过Ｆｒａｎｋ Ｗｏｉｆｅ算法所得的ＤＵＯ配流解进行修正的原则,最后的算例表明由修正后的模型和算法所得到的ＤＵＯ配流解满足ＴＳＣ约束。     

基于手机RFSIM与北斗GIS城市实时交通动态导航平台

目前,从智能交通系统的发展（ITS）来看,国内大多数导航系统为静态导航,缺乏根据实时交通信息进行动态导航的功能.从而提出一种基于手机RFSIM与GIS技术结合的城市实时交通动态导航平台,为出行者提供实时动态的交通信息和导航服务.该系统主要包括实时交通信息采集系统,短时交通预测系统,北斗或GPS定位以及GIS实时导航系统.在已获得的相关技术的支撑下,着力解决在此导航系统中涉及到的交通信息的获取、北斗定位和实时交通动态导航中的关键技术问题,并进行可行性分析.     

SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks

In order to alleviate traffic congestion for vehicles in urban networks, most of current researches mainly focused on signal optimization models and traffic assignment models, or tried to recognize the interaction between signal control and traffic assignment. However, these methods may not be able to provide fast and accurate route guidance due to the lack of individual traffic demands, real-time traffic data and dynamic cooperation between vehicles. To solve these problems, this paper proposes a dynamic and real-time route selection model in urban traffic networks (DR2SM), which can supply a more accurate and personalized strategy for vehicles in urban traffic networks. Combining the preference for alternative routes with real-time traffic conditions, each vehicle in urban traffic networks updates its route selection before going through each intersection. Based on its historical experiences and estimation about route choices of the other vehicles, each vehicle uses a self-adaptive learning algorithm to play congestion game with each other to reach Nash equilibrium. In the route selection process, each vehicle selects the user-optimal route, which can maximize the utility of each driving vehicle. The results of the experiments on both synthetic and real-world road networks show that compared with non-cooperative route selection algorithms and three state-of-the-art equilibrium algorithms, DR2SM can effectively reduce the average traveling time in the dynamic and uncertain urban traffic networks.     

Effectiveness of optimal node assignments in wavelength division multiplexing networks with fixed regular virtual topologies

In this paper, we consider the optimal node assignment problem in wavelength division multiplexing lightwave networks, which is to optimally assign network nodes to the locations in a regular virtual topology through wavelength assignments. Unlike previous work, which concentrated on a single virtual topology, we consider this problem as a class of problems by formulating it as a quadratic assignment problem. As a result, our objective is of a wider scope: identify the factors responsible for effective (or ineffective) node assignments. Optimal node assignments are considered effective if they could significantly improve the performance given by a random node assignment. The performance metric considered here is the average weighted hop distance. Based on a set of carefully designed experiments and analyses, we have concluded that variability in virtual topologies' hop-distance distributions, variability in network traffic distributions, and pattern matching between distance and traffic matrices are major factors in determining the effectiveness of optimal node assignments. In particular, optimal node assignments are most effective for linear virtual topologies and clustered traffic patterns.     

Allocation of freeway ramp metering volumes to optimize corridor performance

A simple macroscopic freeway traffic corridor model is formulated for the purpose of developing optimal allocations of freeway on-ramp metering volumes. The model represents a freeway and a single parallel "equivalent surface street," both unidirectional, which are interconnected by freeway on-ramps. Since a choice of routes is included in the problem formulation, the fairly standard methodology of "traffic assignment" is employed to predict how drivers will react to the on-ramp metering. An explicit traffic assignment algorithm which includes ramp queueing is developed. Both the corridor and traffic assignment models are restricted to stationary traffic patterns. Two specific corridor performance criteria, freeway travel rate and total travel-time rate, are formulated in terms of the model variables. Then the problem of choosing ramp metering volumes to optimize a performance criterion is posed. A computationally efficient suboptimal dynamic programming scheme is developed and illustrated by an example.     

Combined Activity/Travel Choice Models: Time-Dependent and Dynamic Versions

To fully understand and predict travel demand and traffic flow, it is necessary to investigate what drives people to travel. The analysis should examine why, where and when various activities are engaged in, and how activity engagement is related to the spatial and institutional organization of an urban area. In view of this, two combined activity/travel choice models are presented in this paper. The first one is a time-dependent (quasi-dynamic) model for long-term transport planning such as travel demand forecasting, while the other one is a dynamic model for short-term traffic management such as instantaneous flow analysis. The time-dependent model is formulated as a mathematical programming problem for modeling the multinomial logit activity/destination choice and the user equilibrium route choice behavior. It can further be converted to a variational inequality problem. On the other hand, the dynamic model is aimed to find a solution for equilibrium activity location, travel route and departure time choices in queuing networks with multiple commuter classes. It is formulated as a discrete-time, finite-dimensional variational inequality and then converted to an equivalent zero-extreme value minimization problem. Solution algorithms are proposed for these two models and numerical example is presented for the latter. It is shown that the proposed modeling approaches, either based on time-dependent or dynamic traffic assignment principles, provide powerful tools to a wide variety of activity/travel choice problems in dynamic domain.     

A Dantzig-Wolfe Decomposition Based Heuristic Scheme for Bi-level Dynamic Network Design Problem

We present a heuristic to solve the NP-hard bi-level network design problem (NDP). The heuristic is developed based on the Dantzig-Wolfe decomposition principle such that it iteratively solves a master problem and a pricing problem. The master problem is the budget allocation linear program solved by CPLEX to determine the budget allocation and construct a modified cell transmission network for the pricing problem. The pricing problem is the user-optimal dynamic traffic assignment (UODTA) solved by an existing combinatorial algorithm. To facilitate the decomposition principle, we propose a backward connectivity algorithm and complementary slackness procedures to efficiently approximate the required dual variables from the UODTA solution. The dual variables are then employed to augment a new column in the master program in each iteration. The iterative process repeats until a stopping criterion is met. Numerical experiments are conducted on two test networks. Encouraging results demonstrate the applicability of the heuristic scheme on solving large-scale NDP. Though a single destination problem is considered in this paper, the proposed scheme can be extended to solve multi-destination problems as well.     

智能交通系统中Dijkstra算法的高效实现

在智能交通系统中，最佳路径和最短路径的计算是车辆导航功能重要环节，由于越来越多的实时信息参与计算，使得计算行车时间最短的路径变得更频繁，加上道路网络的结点数量和路段数量多，因此，对算法的效率也就要求更高。目前最常用的最佳路径/最短路径算法之一是Dijkstra算法。在智能交通系统中，通过建立相关的数据索引表，可以高效地实现Dijkstra算法，与原始算法相比，大大提高了效率。     

Dynamic interpretation and hoare deduction

In this paper we present a dynamic assignment language which extends the dynamic predicate logic of Groenendijk and Stokhof [1991: 39–100] with assignment and with generalized quantifiers. The use of this dynamic assignment language for natural language analysis, along the lines of o.c. and [Barwise, 1987: 1–29], is demonstrated by examples. We show that our representation language permits us to treat a wide variety of donkey sentences: conditionals with a donkey pronoun in their consequent and quantified sentences with donkey pronouns anywhere in the scope of the quantifier. It is also demonstrated that our account does not suffer from the so-called proportion problem.Discussions about the correctness or incorrectness of proposals for dynamic interpretation of language have been hampered in the past by the difficulty of seeing through the ramifications of the dynamic semantic clauses (phrased in terms of input-output behaviour) in non-trivial cases. To remedy this, we supplement the dynamic semantics of our representation language with an axiom system in the style of Hoare. While the representation languages of barwise and Groenendijk and Stokhof were not axiomatized, the rules we propose form a deduction system for the dynamic assignment language which is proved correct and complete with respect to the semantics.Finally, we define the static meaning of a program of the dynamic assignment language as the weakest condition such that terminates successfully on all states satisfying , and we show that our calculus gives a straightforward method for finding static meanings of the programs of the representation language.