Path Planning for Cooperative Routing of Air-Ground Vehicles

We consider a cooperative vehicle routing problem for surveillance and reconnaissance missions with communication constraints between the vehicles. We propose a framework which involves a ground vehicle and an aerial vehicle; the vehicles travel cooperatively satisfying the communication limits, and visit a set of targets. We present a mixed integer linear programming (MILP) formulation and develop a branch-and-cut algorithm to solve the path planning problem for the ground and air vehicles. The effectiveness of the proposed approach is corroborated through extensive computational experiments on several randomly generated instances

Using the general link transmission model in a dynamic traffic assignment to simulate congestion on urban networks

This article presents two new models of Dynamic User Equilibrium that are particularly suited for ITS applications, where the evolution of vehicle flows and travel times must be simulated on large road networks, possibly in real-time. The key feature of the proposed models is the detail representation of the main congestion phenomena occurring at nodes of urban networks, such as vehicle queues and their spillback, as well as flow conflicts in mergins and diversions. Compared to the simple word of static assignment, where only the congestion along the arc is typically reproduced through a separable relation between vehicle flow and travel time, this type of DTA models are much more complex, as the above relation becomes non-separable, both in time and space. Traffic simulation is here attained through a macroscopic flow model, that extends the theory of kinematic waves to urban networks and non-linear fundamental diagrams: the General Link Transmission Model. The sub-models of the GLTM, namely the Node Intersection Model, the Forward Propagation Model of vehicles and the Backward Propagation Model of spaces, can be combined in two different ways to produce arc travel times starting from turn flows. The first approach is to consider short time intervals of a few seconds and process all nodes for each temporal layer in chronological order. The second approach allows to consider long time intervals of a few minutes and for each sub-model requires to process the whole temporal profile of involved variables. The two resulting DTA models are here analyzed and compared with the aim of identifying their possible use cases. A rigorous mathematical formulation is out of the scope of this paper, as well as a detailed explanation of the solution algorithm. The dynamic equilibrium is anyhow sought through a new method based on Gradient Projection, which is capable to solve both proposed models with any desired precision in a reasonable number of iterations. Its fast convergence is essential to show that the two proposed models for network congestion actually converge at equilibrium to nearly identical solutions in terms of arc flows and travel times, despite their two diametrical approaches wrt the dynamic nature of the problem, as shown in the numerical tests presented here

Development prediction algorithm of vehicle travel time based traffic data

This work bases on encouraging a generous and conceivable estimation for modified an algorithm for vehicle travel times on a highway from the eliminated traffic information using set aside camera image groupings. The strategy for the assessment of vehicle travel times relies upon the distinctive verification of traffic state. The particular vehicle velocities are gotten from acknowledged vehicle positions in two persistent images by working out the distance covered all through elapsed past time doing mollification between the removed traffic flow data and cultivating a plan to unequivocally predict vehicle travel times. Erbil road data base is used to recognize road locales around road segments which are projected into the commended camera images and later distinguished vehicles are assigned to the looking at route segment so instantaneous and current velocities are calculated. All data were effectively processed and visualized using both MATLAB and Python programming language and its libraries

A Note on the Ichoua et al (2003) Travel Time Model.

In this paper we exploit some properties of the travel time model proposed by Ichoua et al (2003), on which most of the current time-dependent vehicle routing literature relies. Firstly, we prove that any continuous piecewise lin- ear travel time model can be generated by an appropriate Ichoua et al (2003) model. We also show that the model parameters can be obtained by solving a system of linear equations for each arc. Then such parameters are proved to be nonnegative if the continuous piecewise linear travel time model satis- es the FIFO property, which allows to interpret them as (dummy) speeds. Finally, we illustrate the procedure through a numerical example. As a by- product, we are able to link the travel time models of a road graph and the associated complete graph over which vehicle routing problems are usually formulated

ARDUINO BASED WIRELESS MOBOT

Increased connectivity and remote monitoring and control mechanisms have revolutionized the field of measurement and automation. The proposed work is to design a system which will integrate a mobile bot with Arduino, and it is also possible with LabVIEW through a gateway to run wirelessly. An autonomous robot vehicle is to travel from source to destination through the wheels which are controlled by processor. This will be helpful launch in the application where human being travel will be difficult to meet the work. The proposed system will be able to follow a path with obstacle avoiding.Further, the vehicle can be integrated with NI instruments and with LabVIEW to make it autonomous. LabVIEW is a graphical programming language gives a platform for the engineers, which is effective and scalable to focus on robotics neglecting the minute implementation details.Â

Evaluation of Coordinated Ramp Metering (CRM) Implemented By Caltrans

Coordinated ramp metering (CRM) is a critical component of smart freeway corridors that rely on real-time traffic data from ramps and freeway mainline to improve decision-making by the motorists and Traffic Management Center (TMC) personnel. CRM uses an algorithm that considers real-time traffic volumes on freeway mainline and ramps and then adjusts the metering rates on the ramps accordingly for optimal flow along the entire corridor. Improving capacity through smart corridors is less costly and easier to deploy than freeway widening due to high costs associated with right-of-way acquisition and construction. Nevertheless, conversion to smart corridors still represents a sizable investment for public agencies. However, in the U.S. there have been limited evaluations of smart corridors in general, and CRM in particular, based on real operational data. This project examined the recent Smart Corridor implementation on Interstate 80 (I-80) in the Bay Area and State Route 99 (SR-99, SR99) in Sacramento based on travel time reliability measures, efficiency measures, and before-and-after safety evaluation using the Empirical Bayes (EB) approach. As such, this evaluation represents the most complete before-and-after evaluation of such systems. The reliability measures include buffer index, planning time, and measures from the literature that account for both the skew and width of the travel time distribution. For efficiency, the study estimates the ratio of vehicle miles traveled vs. vehicle hour traveled. The research contextualizes before-and-after comparisons for efficiency and reliability measures through similar measures from another corridor (i.e., the control corridor of I-280 in District 4 and I-5 in District 3) from the same region, which did not have CRM implemented. The results show there has been an improvement in freeway operation based on efficiency data. Post-CRM implementation, travel time reliability measures do not show a similar improvement. The report also provides a counterfactual estimate of expected crashes in the post-implementation period, which can be compared with the actual number of crashes in the “after” period to evaluate effectiveness

Know Your User: Building a Predictive Model of Consumer Preference for Driverless Cars

INTRODUCTION: This dissertation identifies factors significantly predicting participants\u27 preference for riding in an autonomous vehicle rather than flying on a commercial aircraft. A plethora of research has investigated these two transportation industries independently; however, scarcely any research has considered the impact these two industries will have on each other. Travelers’ preference for riding in an autonomous vehicle rather than a commercial aircraft was investigated through four different scenarios. METHOD: A regression equation was created to predict participants’ preferred travel method and validated through a two-stage process. Stage 1 involved the creation of the regression equation, and a total of 1,008 participants responded to an online survey, providing information on demographics, travel-related behavior, and their preference for riding in an autonomous vehicle rather than flying on a commercial aircraft. Stage 2 involved validation of the regression equation, and 1,008 participants responded to the same online survey. Stage 2 participants’ scores were predicted using the regression equation created in Stage 1. Then, their predicted scores and actual scores were compared to validate the equation throughout four different travel scenarios. RESULTS: In Stage 1, a backward stepwise regression assessed the twenty predictive factors (age, gender, ethnicity, social class, price, perceived value, familiarity, fun factor, wariness of new technology, personality (openness, conscientiousness, extraversion, agreeableness, and neuroticism), general vehicle affect, general airplane affect, vehicle comfort, vehicle external factors, airplane comfort, and airplane external factors). These factors were tested in four different scenarios, which varied only in the length of time participants would spend traveling. CONCLUSION: A predictive model was created for each scenario, and then all four models were validated in Stage 2 using participants’ predicted scores and actual scores. Models were validated using a t-test, correlation, and comparison of cross-validated R2. The most robust model was for the four-hour trip, with six variables significantly predicting participants’ preferred travel method, which accounted for 50.7% of the variance in the model (50.1% adjusted). Upper Social Class, Vehicle Affect, Airplane Affect, and Vehicle Comfort were the only significant predictors throughout all four scenarios. These four predictors will help other researchers and experts in the vehicle industry identify the first adopters of this new technology. The implications of the results and suggestions for future research are discussed