Estimating and exploiting the capacity of urban street networks

The paper deals with the problem of estimating and exploiting traffic capacity of different road elements (link, nodes, network) and presents the results obtained by performing a systematic investigation of the role that the parameters of a microscopic simulation model play on the macroscopic representation of different road elements. An analysis of traffic parameters has been performed using a microsimulation software package to identify the most important parameters affecting the arterial capacity and to calibrate driver's behavior models through macroscopic traffic observations

A Simulation-Optimization Method for Signal Synchronization with Bus Priority and Driver Speed Advisory to Connected Vehicles

The paper introduces a model-based optimization procedure for the design of a control system with signal synchronization, real-time bus priority and green light speed advisory to car drivers. The traffic model simulates car traffic as platoons and bus movements individually. An optimization routine simulates the effect of different bus priority rules, which can be actuated online through bus identification devices and applies a metaheuristic algorithm to optimize signal settings. The macroscopic model and the design method have been applied and also tested in microsimulation on a principal street in Rome with a tram line on a reserved lane. Results obtained show that offline signal optimization and online signal priority can significantly reduce both travel times of bus riders and delays for total traffic. Similarly, speed advisory to drivers, if considered in signal optimization, can improve not only drivers' delays but even transit passengers' delays because it allows more efficient use of the road

Makespan minimizing on multiple travel salesman problem with a learning effect of visiting time

-The multiple traveling salesman problem (MTSP) involves the assignment and sequencing procedure simultaneously. The assignment of a set of nodes to each visitors and determining the sequence of visiting of nodes for each visitor. Since specific range of process is needed to be carried out in nodes in commercial environment, several factors associated with routing problem are required to be taken into account. This research considers visitors’ skill and category of customers which can affect visiting time of visitors in nodes. With regard to learning-by-doing, visiting time in nodes can be reduced. And different class of customers which are determined based on their potential purchasing of power specifies that required time for nodes can be vary. So, a novel optimization model is presented to formulate MTSP, which attempts to ascertain the optimum routes for salesmen by minimizing the makespan to ensure the balance of workload of visitors. Since this problem is an NP-hard problem, for overcoming the restriction of exact methods for solving practical large-scale instances within acceptable computational times. So, Artificial Immune System (AIS) and the Firefly (FA) metaheuristic algorithm are implemented in this paper and algorithms parameters are calibrated by applying Taguchi technique. The solution methodology is assessed by an array of numerical examples and the overall performances of these metaheuristic methods are evaluated by analyzing their results with the optimum solutions to suggested problems. The results of statistical analysis by considering 95% confidence interval for calculating average relative percentage of deviation (ARPD) reveal that the solutions of proposed AIS algorithm has less variation and Its’ confidence interval of closer than to zero with no overlapping with that of FA. Although both proposed meta-heuristics are effective and efficient in solving small-scale problems, in medium and large scales problems, AIS had a better performance in a shorter average time. Finally, the applicability of the suggested pattern is implemented in a case study in a specific company, namely Kalleh

Analysis of Road Safety Speed from Floating Car Data

Intelligent Transportation Systems aims at improving efficiency and safety of the transportation system by acting either on vehicle performances or assisting the driver with information on vehicle and traffic status. Although digital road graphs are available to derive quantitative parameters that describe the road geometry, the information provided usually includes speed limits and repetition of road signs. On the other hand, a huge amount of data is available on individual vehicle speeds and trajectories collected as Floating Car Data (FCD) but they are not combined with road parameters to derive information on how drivers perceive the infrastructure and behave when traveling on it. In the paper, a methodology is presented to evaluate the consistency between drivers' behavior and a theoretical safety speed determined from road geometry. The azimuth profile is progressively built for a road layout, based on the geometry described by a digital graph. Consecutive elements with the constant azimuth variation are identified as circular curves and their radii are computed by circle fitting. The safety speed with respect to longitudinal stability is estimated. The obtained safety speed is then compared to the distribution of speeds observed from about 200 million FCD collected on the regional road network of Latium. The obtained results permit to individuate critical points of the network in terms of road safety

Dynamic O-D demand estimation: Application of SPSA AD-PI method in conjunction with different assignment strategies

This paper examines the impact of applying dynamic traffic assignment (DTA) and quasi-dynamic traffic assignment (QDTA) models, which apply different route choice approaches (shortest paths based on current travel times, User Equilibrium: UE, and system optimum: SO), on the accuracy of the solution of the offline dynamic demand estimation problem. The evaluation scheme is based on the adoption of a bilevel approach, where the upper level consists of the adjustment of a starting demand using traffic measures and the lower level of the solution of the traffic network assignment problem. The SPSA AD-PI (Simultaneous Perturbation Stochastic Approximation Asymmetric Design Polynomial Interpolation) is adopted as a solution algorithm. A comparative analysis is conducted on a test network and the results highlight the importance of route choicemodel and information for the stability and the quality of the offline dynamic demand estimations

Estimating and exploiting the capacity of urban street networks

The paper deals with the problem of estimating and exploiting traffic capacity of different road elements (link, nodes, network) and presents the results obtained by performing a systematic investigation of the role that the parameters of a microscopic simulation model play on the macroscopic representation of different road elements

Optimization of Traffic Signals on Urban Arteries through a Platoon-Based Simulation Model

The paper describes an optimization procedure to synchronize traffic signals along an urban road artery. The solution procedure applies first a genetic algorithm and then a hill climbing algorithm for local adjustments. The fitness function is evaluated by means of a traffic model that simulates platoon progression along the links, their combination and possible queuing at nodes. The potential benefits of the synchronization procedure have been assessed by simulating a real urban artery through the micro-simulation model Transmodeler

Joint Problem of Traffic Signal Synchronization and Bus Priority

Several methods have been developed to allow bus priority with respect to general traffic in urban areas. Among these, signal priority strategies attempt to reduce delay in two ways: by reducing the probability of a transit vehicle encountering a red signal, and, if this does occur, by reducing the wait time until the green signal. The objective of this study is modeling and simulating a mathematical procedure to provide bus priority along a synchronization arterial, through the combination of passive and active bus priority strategies

Coherence analysis of road safe speed and driving behaviour from floating car data

In the Intelligent Transportation Systems, integration of different components of the classical driver-vehicleinfrastructure system is supported by advances in technology and communications. This study presents a general road safety analysis framework that exploits different types of data on traffic, geometry, and accidents to develop a Road Safety Analysis Center and an on-board Road Safety Driver Advisory. The Road Safety Analysis Center considers different sources of data: accident inventories, road geometry, and floating car data, which reveal drivers' behavior. Floating car data are also exploited to derive mathematically the longitudinal parameters of ancient roads, which are crucial to estimate safety conditions in curves. The critical points of the network are revealed by an aggregate analysis of accidents distribution on the roads, while the drivers' behaviour is addressed on a disaggregated level, by the evaluation of speeds distributions with a dense spatial detail. The comparison between speeds distributions, safety conditions, and accident occurrence is useful to individuate the portions of the network to be enforced with safety measures and support drivers with an advanced onboard speed advisory system. This methodology is applied to several extra-urban roads in the Latium region, Italy, to individuate roads with higher values of critical indices