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Description

A simple traffic assignment problem: - Move 5 units of stuff from A to B as quickly as possible, using one or more of the N paths p1..pN between A and B. - The delay d on each path is a function of the volume of traffic on the path: d(p) = a(p) + x(p) + x(p)**1.5 This is a simple example showing how VI can be used to compute effective delay*, i.e. the maximum delay over all paths with nonzero flow, or the time it takes for all the stuff to reach the destination. For an example involving a nontrivial network see the traffic2 model in this library. Contributor: Steven Dirkse, January 2009

Small Model of Type : VI

Category : GAMS EMP library

Main file : traffic.gms

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traffic assignment source code

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Remove a code repository from this paper, mark the official implementation from paper authors, add a new evaluation result row, remove a task, add a method, remove a method, edit datasets, dynamic traffic assignment for electric vehicles.

10 Jul 2022  ·  Lukas Graf , Tobias Harks , Prashant Palkar · Edit social preview

We initiate the study of dynamic traffic assignment for electrical vehicles addressing the specific challenges such as range limitations and the possibility of battery recharge at predefined charging locations. We pose the dynamic equilibrium problem within the deterministic queueing model of Vickrey and as our main result, we establish the existence of an energy-feasible dynamic equilibrium. There are three key modeling-ingredients for obtaining this existence result: * We introduce a walk-based definition of dynamic traffic flows which allows for cyclic routing behavior as a result of recharging events en route. * We use abstract convex feasibility sets in an appropriate function space to model the energy-feasibility of used walks. * We introduce the concept of capacitated dynamic equilibrium walk-flows which generalize the former unrestricted dynamic equilibrium path-flows. Viewed in this framework, we show the existence of an energy-feasible dynamic equilibrium by applying an infinite dimensional variational inequality, which in turn requires a careful analysis of continuity properties of the network loading as a result of injecting flow into walks. We complement our theoretical results by a computational study in which we design a fixed-point algorithm computing energy-feasible dynamic equilibria. We apply the algorithm to standard real-world instances from the traffic assignment community illustrating the complex interplay of resulting travel times, energy consumption and prices paid at equilibrium.

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  • 7. Traffic Assignment
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7. Traffic Assignment ¶

Along with a network data model, traffic assignment is the most technically challenging portion to develop in a modeling platform, especially if you want it to be FAST . In AequilibraE, we aim to make it as fast as possible, without making it overly complex to use, develop and maintain (we know how subjective complex is).

AequilibraE has had efficient multi-threaded All-or-Nothing (AoN) assignment for a while, but since the Method-of-Successive-Averages, Frank-Wolfe, Conjugate-Frank-Wolfe and Biconjugate-Frank-Wolfe are new in the software, it should take some time for these implementations to reach full maturity.

7.1. Performing traffic assignment ¶

For a comprehensive use case for the traffic assignment module, please see the Traffic assignment section of the use cases page.

7.1.1. Traffic Assignment Class ¶

Traffic assignment is organized within a object new to version 0.6.1 that includes a small list of member variables which should be populated by the user, providing a complete specification of the assignment procedure:

classes : List of objects Traffic class , each of which are a completely specified traffic class

vdf : The Volume delay function (VDF) to be used

vdf_parameters : The parameters to be used in the volume delay function, other than volume, capacity and free flow time

time_field : The field of the graph that corresponds to free-flow travel time . The procedure will collect this information from the graph associated with the first traffic class provided, but will check if all graphs have the same information on free-flow travel time

capacity_field : The field of the graph that corresponds to link capacity . The procedure will collect this information from the graph associated with the first traffic class provided, but will check if all graphs have the same information on free-flow travel time

algorithm : The assignment algorithm to be used. e.g. “all-or-nothing” or “bfw”

Assignment parameters such as maximum number of iterations and target relative gap come from the global software parameters, that can be set using the Parameters module

There are also some strict technical requirements for formulating the multi-class equilibrium assignment as a contrained convex optimization problem, as we have implemented it. These requirements are loosely listed in _technical_requirements_multi_class .

If you want to see the assignment log on your terminal during the assignment, please look in the Logging section of the use cases.

To begin building the assignment it is easy:

7.1.1.1. Volume Delay Function ¶

For now, the only available VDF function in AequilibraE is the BPR, but more functions will be added as needed/requested/possible.

\(CongestedTime_{i} = FreeFlowTime_{i} * (1 + \alpha * (\frac{Volume_{i}}{Capacity_{i}})^\beta)\)

Setting the volume delay function is one of the first things you should do after instantiating an assignment problem in AequilibraE, and it is as simple as:

The implementation of the VDF functions in AequilibraE is written in Cython and fully multi-threaded, and therefore descent methods that may evaluate such function multiple times per iteration should not become unecessarily slow, especially in modern multi-core systems.

7.1.2. Traffic class ¶

The Traffic class object holds all the information pertaining to a specific traffic class to be assigned. There are three pieces of information that are required in the composition of this class:

graph - It is the Graph object corresponding to that particular traffic class/ mode

matrix - It is the AequilibraE matrix with the demand for that traffic class, but which can have an arbitrary number of user-classes, setup as different layers of the matrix object (see the Setting multiple user classes before assignment

pce - The passenger-car equivalent is the standard way of modelling multi-class traffic assignment equilibrium in a consistent manner (see [4] for the technical detail), and it is set to 1 by default. If the pce for a certain class should be different than one, one can make a quick method call.

To add traffic classes to the assignment instance it is just a matter of making a method call:

7.1.3. setting VDF Parameters ¶

Parameters for VDF functions can be passed as a fixed value to use for all links, or as graph fields. As it is the case for the travel time and capacity fields, VDF parameters need to be consistent across all graphs.

Because AequilibraE supports different parameters for each link, its implementation is the most general possible while still preserving the desired properties for multi-class assignment, but the user needs to provide individual values for each link OR a single value for the entire network.

Setting the VDF parameters should be done AFTER setting the VDF function of choice and adding traffic classes to the assignment, or it will fail .

To choose a field that exists in the graph, we just pass the parameters as follows:

To pass global values, it is simply a matter of doing the following:

7.1.4. Setting final parameters ¶

There are still three parameters missing for the assignment.

Capacity field

Travel time field

Equilibrium algorithm to use

Finally, one can execute assignment:

Convergence criteria is discussed below.

7.2. Multi-class Equilibrium assignment ¶

By introducing equilibrium assignment [1] with as many algorithms as we have, it makes sense to also introduce multi-class assignment, adding to the pre-existing capability of assigning multiple user-classes at once. However, multi-class equilibrium assignments have strict technical requirements and different equilibrium algorithms have slightly different resource requirements.

Our implementations of the conjudate and Biconjugate-Frank-Wolfe methods should be inherently proportional [6], but we have not yet carried the appropriate testing that would be required for an empirical proof

7.2.1. Cost function ¶

It is currently not possible to use custom cost functions for assignment, and the only cost function available to be minimized is simply travel time.

7.2.2. Technical requirements ¶

This documentation is not intended to discuss in detail the mathematical requirements of multi-class traffic assignment, which can be found discussed in detail on Zill et all.

A few requirements, however, need to be made clear.

All traffic classes shall have identical free-flow travel times throughout the network

Each class shall have an unique Passenger Car Equivalency (PCE) factor

Volume delay functions shall be monotonically increasing. Well behaved functions are always something we are after

For the conjugate and Biconjugate Frank-Wolfe algorithms it is also necessary that the VDFs are differentiable.

7.2.3. Convergence criteria ¶

Convergence in AequilibraE is measured solely in terms of relative gap, which is a somewhat old recommendation [5], but it is still the most used measure in practice, and is detailed below.

\(RelGap = \frac{\sum_{a}V_{a}^{*}*C_{a} - \sum_{a}V_{a}^{AoN}*C_{a}}{\sum_{a}V_{a}^{*}*C_{a}}\)

The algorithm’s two stop criteria currently used are the maximum number of iterations and the target Relative Gap, as specified above. These two parameters are collected directly from the Parameter File , described in detail in the parameter_assignment section.

In order to override the parameter file values, one can set the assignment object member variables directly before execution.

7.2.4. Algorithms available ¶

All algorithms have been implemented as a single software class, as the differences between them are simply the step direction and step size after each iteration of all-or-nothing assignment, as shown in the table below

7.2.4.1. Method of Successive Averages ¶

This algorithm has been included largely for hystorical reasons, and we see very little reason to use it. Yet, it has been implemented with the appropriate computation of relative gap computation and supports all the analysis features available.

7.2.4.2. Frank-Wolfe (FW) ¶

The implementation of Frank-Wolfe in AequilibraE is extremely simple from an implementation point of view, as we use a generic optimizer from SciPy as an engine for the line search, and it is a standard implementation of the algorithm introduced by LeBlanc in 1975 [2].

7.2.4.3. Conjugate Frank-Wolfe ¶

The conjugate direction algorithm was introduced in 2013 [3], which is quite recent if you consider that the Frank-Wolfe algorithm was first applied in the early 1970’s, and it was introduced at the same as its Biconjugate evolution, so it was born outdated.

7.2.4.4. Biconjugate Frank-Wolfe ¶

The Biconjugate Frank-Wolfe algorithm is currently the fastest converging link- based traffic assignment algorithm used in practice, and it is the recommended algorithm for AequilibraE users. Due to its need for previous iteration data, it requires more memory during runtime, but very large networks should still fit nicely in systems with 16Gb of RAM.

7.2.5. Implementation details & tricks ¶

A few implementation details and tricks are worth mentioning not because it is needed to use the software, but because they were things we grappled with during implementation, and it would be a shame not register it for those looking to implement their own variations of this algorithm or to slight change it for their own purposes.

The relative gap is computed with the cost used to compute the All-or-Nothing portion of the iteration, and although the literature on this is obvious, we took some time to realize that we should re-compute the travel costs only AFTER checking for convergence.

In some instances, Frank-Wolfe is extremely unstable during the first iterations on assignment, resulting on numerical errors on our line search. We found that setting the step size to the corresponding MSA value (1/ current iteration) resulted in the problem quickly becoming stable and moving towards a state where the line search started working properly. This technique was generalized to the conjugate and biconjugate Frank-Wolfe algorithms.

7.2.5.1. Opportunities for multi-threading ¶

Most multi-threading opportunities have already been taken advantage of during the implementation of the All-or-Nothing portion of the assignment. However, the optimization engine using for line search, as well as a few functions from NumPy could still be paralellized for maximum performance on system with high number of cores, such as the latest Threadripper CPUs. These numpy functions are the following:

A few NumPy operations have already been parallelized, and can be seen on a file called parallel_numpy.pyx if you are curious to look at.

Most of the gains of going back to Cython to paralelize these functions came from making in-place computation using previously existing arrays, as the instantiation of large NumPy arrays can be computationally expensive.

7.2.5.2. References ¶

[1] Wardrop J. G. (1952) “Some theoretical aspects of road traffic research.” Proc. Inst. Civil Eng. 1 Part II, pp.325-378.

[2] LeBlanc L. J., Morlok E. K. and Pierskalla W. P. (1975) “An efficient approach to solving the road network equilibrium traffic assignment problem” Transpn Res. 9, 309-318.

[3] Maria Mitradjieva and Per Olov Lindberg (2013)”The Stiff Is Moving—Conjugate Direction Frank-Wolfe Methods with Applications to Traffic Assignment”, Transportation Science 2013 47:2, 280-293

[4] Zill, J., Camargo, P., Veitch, T., Daisy,N. (2019) “Toll Choice and Stochastic User Equilibrium: Ticking All the Boxes”, Transportation Research Record, Vol 2673, Issue 4 DOI

[5] Rose, G., Daskin, M., Koppelman, F. (1988) “An examination of convergence error in equilibrium traffic assignment models”, Transportation Res. B, Vol 22 Issue 4, PP 261-274 DOI

[6] Florian, M., Morosan, C.D. (2014) “On uniqueness and proportionality in multi-class equilibrium assignment”, Transportation Research Part B, Volume 70, pg 261-274 DOI

7.3. Handling the network ¶

The other important topic when dealing with multi-class assignment is to have a single consistent handling of networks, as in the end there is only physical network being handled, regardless of access differences to each mode (e.g. truck lanes, High-Occupancy Lanes, etc.). This handling is often done with something called a super-network .

7.3.1. Super-network ¶

We deal with a super-network by having all classes with the same links in their sub-graphs, but assigning b_node identical to a_node for all links whenever a link is not available for a certain user class. It is slightly less efficient when we are computing shortest paths, but a LOT more efficient when we are aggregating flows.

The use of the AequilibraE project and its built-in methods to build graphs ensure that all graphs will be built in a consistent manner and multi-class assignment is possible.

7.4. Numerical Study ¶

Similar to other complex algorthms that handle a large amount of data through complex computations, traffic assignment procedures can always be subject to at least one very reasonable question: Are the results right?

For this reason, we have used all equilibrium traffic assignment algorithms available in AequilibraE to solve standard instances used in academia for comparing algorithm results, some of which have are available with highly converged solutions (~1e-14): https://github.com/bstabler/TransportationNetworks/

7.4.1. Sioux Falls ¶

Network has:

Sioux Falls MSA 500 iterations

7.4.2. Anaheim ¶

Anaheim MSA 500 iterations

7.4.3. Winnipeg ¶

Winnipeg MSA 500 iterations

The results for Winnipeg do not seem extremely good when compared to a highly, but we believe posting its results would suggest deeper investigation by one of our users :-),

7.4.4. Barcelona ¶

Links: 2,522

Nodes: 1,020

Barcelona MSA 500 iterations

7.4.5. Chicago Regional ¶

Chicago MSA 500 iterations

7.5. Convergence Study ¶

Besides validating the final results from the algorithms, we have also compared how well they converge for the largest instance we have tested (Chicago Regional), as that instance has a comparable size to real-world models.

Algorithm convergence comparison

Not surprinsingly, one can see that Frank-Wolfe far outperforms the Method of Successive Averages for a number of iterations larger than 25, and is capable of reaching 1.0e-04 just after 800 iterations, while MSA is still at 3.5e-4 even after 1,000 iterations.

The actual show, however, is left for the Biconjugate Frank-Wolfe implementation, which delivers a relative gap of under 1.0e-04 in under 200 iterations, and a relative gap of under 1.0e-05 in just over 700 iterations.

This convergence capability, allied to its computational performance described below suggest that AequilibraE is ready to be used in large real-world applications.

7.6. Computational performance ¶

Running on a Thinkpad X1 extreme equipped with a 6 cores 9750H CPU and 32Gb of 2667Hz RAM, AequilibraE performed 1,000 iterations of Frank-Wolfe assignment on the Chicago Network in just under 46 minutes, while Biconjugate Frank Wolfe takes just under 47 minutes.

During this process, the sustained CPU clock fluctuated between 3.05 and 3.2GHz due to the laptop’s thermal constraints, suggesting that performance in modern desktops would be better

7.7. Noteworthy items ¶

The biggest opportunity for performance in AequilibraE right now it to apply network contraction hierarchies to the building of the graph, but that is still a long-term goal

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Dynamic Traffic Assignment

Introduction .

The Transportation Authority uses a Dynamic Traffic Assignment (DTA) model to give planners a more fine-grained view of transportation system performance and a better understanding of traffic patterns around San Francisco. DTA is an intermediate-scale simulation of both autos and transit vehicles that the Transportation Authority has previously piloted for a few projects in the northwestern section of the city. In 2012, the Transportation Authority completed the development of a citywide DTA model with the help of a grant from the Federal Highway Administration.

Using DTA for Transportation Modeling in San Francisco 

The Transportation Authority needs a tool that can effectively predict changes in travel across our network in order to evaluate the effects of various transportation projects. While the Transportation Authority's SF-CHAMP travel demand forecasting model has a very detailed understanding of the intricate travel demand decisions of individuals for our entire region, it is limited in its ability to capture the detailed behavior of traffic and buses as they respond to traffic signal timing, queue formation, and other detailed operational information. Just as more sophisticated activity-based travel demand models (such as SF-CHAMP) have better sensitivity to non-standard transportation improvements than traditional four-step models, DTA provides the sensitivity and robustness needed to analyze traffic operational strategies and their outcomes, such as travel time reliability. 

Examples where the Transportation Authority has already used DTA are:

  • Determining where cars would reroute during long-term road closures for construction.
  • Evaluating bus travel times and auto route shifts for a Bus Rapid Transit project.

The DTA Anyway Project

The DTA Anyway project, funded by a grant from the Federal Highway Administration, was completed in November 2012.

The project simultaneously:

Created an open-source code base to lower the barrier to entry to large-scale DTA modeling and reduce the amount of effort necessary for maintaining it.

Researched how a large-scale DTA model works, both by itself and when integrated with an activity-based travel demand model.

Developed a working and calibrated DTA model of San Francisco County for the numerous projects that would benefit from it.

The DTA Anyway team shared the ins and outs of the project at the DTA Anyway project website , which was revised several times a week with new code and project updates.

DTA Anyway was a huge undertaking, requiring a massive amount of data synthesis and maintenance as well as model calibration to make sure the model is behaving reasonably. While there are a few large-scale DTA networks in existence, most are built to evaluate a single project or as a research demonstration. Ours is likely the first DTA model of this scale built to be continuously maintained and used by a public agency, and by doing it "out in the open" on our project website, we hope to lower the barrier to entry for other agencies.

Transportation Authority Reports 

DTA Final Report, 2012  (PDF) DTA Calibration and Validation Report, 2012  (PDF) DTA Future Research Topics (PDF)

Publications and Presentations

Dynamic Traffic Assignment Primer, June 2011 Feasibility of using Citywide DTA in San Francisco, 2009  (PDF) DTA Model Development Webinar Presentation, 2012  (PDF)

[email protected] 

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Traffic Control Management System C++ Project

Rajendra Bohara

Developed in C++ language, Traffic Control Management System is a management application developed for the purpose of controlling traffic problems. As a final year project, it can be used for the demonstration of practical use of C++ programming language along with its features. The scope of this project is big as it can be used in any urban roads and junctions for the appropriate management of traffic.

The source code of Traffic Control Management System has been written in C++ programming language with the use of video graphics. You can download the source code, executable file and basic description of this project from the link provided in this post. To give you an insight into the project, here I have briefly described the functions, working mechanism and features of this project.

Download Traffic Control Management System C++ Project  with Source Code

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About Traffic Control Management Project:

The project source code consists of two cpp files( main.cpp and caliberate.cpp ) and a user defined header file( caleberate.h ). The main.cpp file in the source code integrates all files and makes do the designed task of the project.

In Traffic Control Management System, the major function of user defined header file is to make the system capture the video of the traffic lanes and to analyze them. The caliberate.cpp receives the analyzed data of running traffic and it takes the appropriate decision for traffic control.

The application detects traffic by using opencv. The algorithm provides necessary traffic information required for analysis and decision making in traffic management. The major information to be detected and analyzed for traffic management by this application are:

  • Average movement of vehicles
  • Percentage of area covered by vehicles on the road

This   software analyses the traffic for each lane decides to divide the traffic based on type of vehicle. In order to reduce traffic congestion, the small vehicles are moved in one lane and heavier vehicle vehicles are moved in other.

Before the installation of application in road, it needs to be tested. In road, the application first captures video of running traffic and analyzes, but while testing on computer the video file may not present. So, follow the steps given below:-

  • Place a video file in same directory of executable file.
  • Rename the video file as “traffic.avi”
  • Build and Run the code.
  • In “Draw Polygon” Window choose four points.

After performing these steps, the Traffic Control Management application separates the lanes for respective type of vehicles as per algorithm embedded in the code. Finally, you will see a console output of separated lane as shown below.

The conventional way of controlling traffic at junctions and other sectors of urban road is growing ineffective with the passage of time due to increase in the number of vehicles on road. At a busy junction, it is really a very complex task for a single traffic police to pass all the vehicles safely.

Screenshot/Diagram:

Traffic Control Management System in C++

Also see, City Bus Management System Smart City Project More Projects in C++

The use of computer based system to control traffic is the demand of time. So, the application like Traffic Control Management Project is essential for easy and scientific traffic control in modern society. I hope this project will help you in doing your final year project. If you have any question, bring them up from the comments section.

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University Management System Project

please send coading on my email.

i need in coading project for this java

please send the code no any links found

Please, How do I find package OpenCV and cv.hpp file?

please send source code on my email.

Hey, first try the download link. If it isn’t working, let me know. I’ll mail it to you.

i need a source code for this

Please download the code from the link in the article.

wht are the software used for its GUI Compilation. Please give the desired steps to work or seting up of the projct

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  • Sep 11, 2020

Traffic Management System Using Java

Updated: Mar 25, 2021

The goal of this assignment is to implement a set of classes and interfaces1 to be used to create a simulation of a traffic management system. You will implement precisely the public and protected items described in the supplied documentation (no extra public/protected members or classes).

Private members may be added at your own discretion.

Language requirements:

Java version 13,

Introduction

In this assignment you will finish building a simple simulation of a traffic management system (TMS). A traffic management system monitors traffic flow in a region and adjusts traffic signals to optimise traffic flow. A TMS uses different types of sensors and signals to monitor and manage traffic flow. In the first assignment you implemented the core model for the TMS. In the second assignment you will implement some of the more advanced logic to provide a very simple simulation for the TMS.

In addition to the pressure pads and speed cameras from assignment one, you willl add a vehicle count sensor. It counts vehicles passing a location and reports the traffic flow as the number of vehicles in a time period. You need to integrate this new type of sensor into the system. This is an example of a common situation when building a large system. New features need to be added to the system. A well designed system that uses interfaces to define an API means it should be simple to add the new feature.

In assignment one, you implemented traffic lights and electronic speed signs and attached them to a route. In assignment two you will provide logic to coordinate traffic lights at intersections.

The TMS monitors sensors along routes and manages signals on routes, and at intersections, to optimise traffic flow. In assignment one, the network of routes was implicitly defined by your test code and SimpleDisplay. In assignment two you will implement the logic for the TMS to maintain a network of routes. This includes the ability to load a network from a data file and save a modified network to a file.

Monitoring and managing congestion requires sophisticated logic in a real TMS. In assignment one congestion was simply reported by each sensor. In assignment two you will implement logic for congestion calculators. These take the congestion data from a set of sensors and determine overall congestion for the route(s) covered by the sensors.

The approach taken is to define a CongestionCalculator interface that provides an API. Different classes can implement this inter- face to provide different options for the logic of determining congestion. This is another example of a common approach to designing flexibility into the system’s structure.

When implementing the assignment you need to remember that it is implementing a simulation of the TMS and not the real TMS. Interfaces are provided for the sensors to allow easy replacement of sensor implementations in the program. You will not be collecting data from real sensors but will be implementing classes that demonstrate the behaviour of sensors. They store a set of data values that are used to simulate the sensors returning different values over time. Signals are simple simulations of real signals, in that they only store the current state of the signal and allow the route to update the signal.

To manage simulation of time, there is a TimedItem interface and a TimedItemManager class, which you implemented in assignment one. Sensors implement the TimedItem interface, as they are items which need to react to timed events. TimedItemManager stores all the TimedItem ob- jects in the application. The simulation’s GUI tracks time passing in MainView.run() and it invokes MainViewModel.tick() once per second. The tick method calls the TimedItemManager’s oneSecond method, which sends the oneSecond message to all TimedItems. This approach of tracking the passage of time and invoking an action on all relevant objects once per second was the reason that TimedItemManager is implemented as a singleton2

A simple GUI has been provided to you as part of the provided code. It is in the tms.display package. It will not work until you have implemented the other parts of the assignment that it uses.

The GUI has been implemented using JavaFX and consists of three classes and an enum. MainView creates the main window for the TMS GUI. StructureView displays the structure of the traffic network. MainViewModel represents the TMS model that is to be displayed. The TMS application is initialised and started by the Launcher class in the tms package. It loads the traffic network data and creates the GUI. Most of the GUI code has been provided to you. In MainViewModel you need to implement some of the logic that is executed by events in the simulation and to handle keyboard input for the main application’s window.

The functionality you need to implement in MainViewModel is to:

Save the state of the network to a file in response to the user selecting the save command.

This is to be implemented in MainViewModel.save().

Allow the simulation’s execution to be paused and unpaused. This is to be implemented in MainViewModel.togglePaused().

Process time passing in the simulation. This is to be implemented in MainViewModel.tick().

Keyboard input is handled by the accept method in the MainViewModel class. It needs to process input from the user in the main window to perform actions in the simulation. Pressing the ‘P’ key will toggle whether the simulation is paused or not. The ‘Q’ key will quit the simulation.

The ‘S’ key will save the current network to a file called “DefaultSave.txt”. A shell for this method has been provided because it is already hooked into the GUI.

Persistent Data

You need to implement loading a network from a data file. The JavaDoc for the loadNetwork method in the NetworkInitialiser class describes the format of a network data file. Saving a network is done by the save method in the MainViewModel class. A network data file is structured as follows:

The first line is the number of intersections (ni) in the file.

The second line is the number of routes in the file.

The third line is the duration of a yellow light.

The following ni lines are the intersection details.

The first part of an intersection line is its id.

This is optionally followed by a ‘:’, a duration, another ‘:’, and a sequence of intersection ids which are separated by commas.

The final set of lines are the route details, including any sensors on the routes.

– Each route is on a separate line. The sensors for a route are on the lines immediately

after the line for the route.

– A route is described by the id of the from intersection, followed by a ‘:’, then the id of

the to intersection, followed by a ‘:’, then the default speed for the route, followed by a

‘:’, then the number of sensors on the route, then optionally a ‘:’ and the speed of the

electronic speed sign on the route if it has one.

– If the route has any sensors, each sensor follows on separate lines.

– The first part of a sensor line is its type ‘PP’, ‘SC’ or ‘VC’. This is followed by a ‘:’,

then its threshold value, a ‘:’, and then a comma separated list of the data values used

to simulate the data returned by the sensor.

Any line that starts with a semi-colon ‘;’ is a comment and is to be ignored when reading the data from the file.

Attempting to read an invalid network data file should throw an InvalidNetworkException.

An example data file, called demo.txt, is provided in your repository in the networks directory. It corresponds to the diagram below.

traffic assignment source code

Supplied Material

This task sheet.

An example network data file.

Code specification document (Javadoc)

A Subversion repositiory for submitting your assignment called ass2.

A simple graphical user interface for the simulation, which is in the display package.

A sample solution for the first assignment. You are to use this as the base for your implementation of the second assignment. As the first step in the assignment you should create a new project by checking out the ass2 repository from Subversion.

Code specifications are an important tool for developing code in collaboration with other people. Although assignments in this course are individual, they still aim to prepare you for writing code to a strict specification by providing a specification document (in Java, this is called Javadoc). You will need to implement the specification precisely as it is described in the specification document.

The Javadoc can be viewed in either of the two following ways:

1. Open https://csse2002.uqcloud.net/assignment/2/ in your web browser. Note that this will only be the most recent version of the Javadoc.

2. Navigate to the relevant assignments folder under Assessment on Blackboard and you will be able to download the Javadoc .zip file containing html documentation. Unzip the bundle somewhere, and open docs/index.html with your web browser.

Tags in the Javadoc indicate what code has been implemented in assignment one and what code you need to implement in assignment two. Some code from assignment one will need to be modified. There are tags indicating places where you can expect to modify the assignment one code but these are not guaranteed to be all of the places where you may end up modifying code from assignment one.

1. Implement the classes and methods described in the Javadoc as being requried for assignment two.

2. Implement the indicated features of the user interface.

3. Write JUnit 4 tests for all the methods in the following classes:

AveragingCongestionCalculator (in a class called AveragingCongestionCalculatorTest)

IntersectionLights (in a class called IntersectionLightsTest)

NetworkInitialiser (in a class called NetworkInitialiserTest)

Submission is via your Subversion repository. You must ensure that you have committed your code to your repository before the submission deadline. Code that is submitted after the deadline will not be marked. Failure to submit your code through your repository will result in it not being marked. Details for how to submit your assignment are available in the Version Control Guide.

Your repository url is:

https://source.eait.uq.edu.au/svn/csse2002-s???????/trunk/ass2 — CSSE2002 students

https://source.eait.uq.edu.au/svn/csse7023-s???????/trunk/ass2 — CSSE7023 students

Your submission should have the following internal structure:

src/ folders (packages) and .java files for classes described in the Javadoc

test/ folders (packages) and .java files for the JUnit test classes

A complete submission would look like:

src/tms/congestion/AveragingCongestionCalculator.java

src/tms/congestion/CongestionCalculator.java

src/tms/display/ButtonOptions.java

src/tms/display/MainView.java

src/tms/display/MainViewModel.java

src/tms/display/StructureView.java

src/tms/intersection/Intersection.java

src/tms/intersection/IntersectionLights.java

src/tms/network/Network.java

src/tms/network/NetworkInitialiser.java

src/tms/route/Route.java

src/tms/route/SpeedSign.java

src/tms/route/TrafficLight.java

src/tms/route/TrafficSignal.java

src/tms/sensors/DemoPressurePad.java

src/tms/sensors/DemoSensor.java

src/tms/sensors/DemoSpeedCamera.java

src/tms/sensors/DemoVehicleCount.java

src/tms/sensors/PressurePad.java

src/tms/sensors/Sensor.java

src/tms/sensors/SpeedCamera.java

src/tms/sensors/VehicleCount.java

src/tms/util/DuplicateSensorException.java

src/tms/util/IntersectionNotFoundException.java

src/tms/util/InvalidNetworkException.java

src/tms/util/InvalidOrderException.java

src/tms/util/RouteNotFoundException.java

src/tms/util/TimedItem.java

src/tms/util/TimedItemManager.java

src/tms/Launcher.java

test/tms/congestion/AveragingCongestionCalculatorTest.java

test/tms/intersection/IntersectionLightsTest.java

test/tms/network/NetworkInitialiserTest.java

test/tms/JdkTest.java

Ensure that your assignments correctly declare the package they are within. For example, CongestionCalculator.java should declare package tms.congestion.

Do not submit any other files (e.g. no .class files). Note that AveragingCongestionCalculatorTest, IntersectionLightsTest and NetworkInitialiserTest will be compiled without the rest of your files.

If you are looking solution of this project assignment then you can contact us at below contact detail, we will also provide other java related technology help: JavaFx, Spring, J2EE, etc. [email protected]
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JASON RANTZ

Rantz: Seattle English students told it’s ‘white supremacy’ to love reading, writing

Feb 14, 2024, 7:08 PM

Image: Lincoln High School in Seattle teachings on white supremacy leads to controversy. Seattle wh...

Lincoln High School in Seattle teachings on white supremacy leads to controversy. (School photo courtesy of the school district website; quiz images provided by a parent in the school district)

(School photo courtesy of the school district website; quiz images provided by a parent in the school district)

Jason Rantz's Profile Picture

BY JASON RANTZ

The Jason Rantz Show, 3pm-7pm on KTTH

Students in a Seattle English class were told that their love of reading and writing is a characteristic of “white supremacy,” in the latest Seattle Public Schools high school controversy. The lesson plan has one local father speaking out, calling it “educational malpractice.”

As part of the Black Lives Matter at School Week, World Literature and Composition students at Lincoln High School were given a handout with definitions of the “9 characteristics of white supremacy,” according to the father of a student. Given the subject matter of the class, the father found it odd this particular lesson was brought up.

The Seattle high schoolers were told that “Worship of the Written Word” is white supremacy because it is “an erasure of the wide range of ways we communicate with each other.” By this definition, the very subject of World Literature and Composition is racist. It also chides the idea that we hyper-value written communication because it’s a form of “honoring only what is written and even then only what is written to a narrow standard, full of misinformation and lies.” The worksheet does not provide any context for what it actually means.

“I feel bad for any students who actually internalize stuff like this as it is setting them up for failure,” the father explained to the Jason Rantz Show on KTTH.

More from Jason Rantz: Communist Seattle teacher breaks silence to support Hamas, claim ‘ACAB’

Everything is ‘white supremacy’ at Seattle Public Schools

The father asked to remain anonymous for fear of retribution against his child by Seattle Public Schools. He said the other pieces of the worksheet were equally disturbing.

The worksheet labels “objectivity,” “individualism,” and “perfectionism” as white supremacy. If students deny their own racism — or that any of the nine characteristics are legitimately racist — is also white supremacy. Denialism or being overly defensive is a racist example of an “entitlement to name what is an [sic] isn’t racism and that those with power have a right to be shielded from the stresses of antiracist work.”

The father argues the concepts are “incoherent and cannot stand any sort of reasoned analysis.” And he notes that it’s set up to ensure students accept every concept without ever questioning the claims.

“How is a 15-year-old kid supposed to object in class when ‘denial and defensiveness’ is itself a characteristic of white supremacy? This is truly educational malpractice.”

traffic assignment source code

Terms and definitions regarding white supremacy given to Lincoln High students.

White students told to apologize in yet another Seattle high school controversy

Another aspect of the white supremacy lesson at this Seattle school involved a video titled “Getting Called Out: How to Apologize” by Franchesca Ramsey. It’s reportedly presented in the context of white students expressing what the teacher views as “white supremacy.”

“Getting called out, in this context of this video, is when you say or do something that upholds the oppression of a marginalized group of people,” Ramsey says.

Ramsey says her advice is about becoming an ally and “doing the right thing.” She explains you shouldn’t “get defensive” by denying you’re oppressing marginalized people, even if you’re not actually oppressing marginalized people.

“What you really need to do is listen because this is where the other person is hopefully going to explain to you what you did wrong and how you can explain it,” she says.

In the context of the worksheet on white supremacy, it seems clear that students must merely accept that they are upholding oppression. Using the worksheet, if a student defends independence or a love of reading and writing, that student is supposed to accept that it’s white supremacist thinking and stop acting independently or loving to read and write.

traffic assignment source code

The worksheet on white supremacy.

Father says Seattle Public Schools isn’t serving students

The father says he taught his son to be on the lookout for this kind of Radical Left indoctrination. It’s why his son flagged the worksheets to him. But he notes that the curriculum doesn’t exactly help his kid on the subject he’s supposed to be learning.

“My problem with this curriculum is that this is supposed to be a writing and literature class and lessons like these do nothing to help my kid become a better writer,” the father explained. “I’m sure Lincoln administration will point to the high ELA proficiency scores but the high proportion of HCC [highly capable] kids (40% of the student body) is a big factor. With so many smart, hard working kids (white supremacists) it’s easy to support these luxury beliefs but system-wide only 63% of kids are proficient in English. Is this really the best use of class time? ”

The father also wonders how many students will fall for this toxic thinking across Seattle schools where concepts around white supremacy are so clearly partisan.

“I feel bad for any students who actually internalize stuff like this as it is setting them up for failure,” he said.

Seattle Public Schools spokespeople provided their normal response to requests for comment: none.

traffic assignment source code

‘How do white supremacy characteristics show up in your personal lives?’ was a question in a worksheet given to Lincoln High students.

Listen to the Jason Rantz Show on weekday afternoons from 3-6 p.m. on KTTH 770 AM (HD Radio 97.3 FM HD-Channel 3). Subscribe to the  podcast here . Follow Jason on  X, formerly known as Twitter ,  Instagram  and  Facebook .

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A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking

Lemma1/Multimodal-DUE

Folders and files, repository files navigation.

Implemented by Wei Ma and Xidong Pi (AlanPi1992), advised by Sean Qian, Civil and environmental engineering, Carnegie Mellon University.

Requirements

  • cvxopt 1.1.9
  • numpy 1.14.2
  • MNMAPI: MNMAPI is a traffic simulation library developed by MAC in CMU, please refer to https://github.com/Lemma1/MAC-POSTS and http://mac-posts.com/
  • MNM_mcnb: the folder interface to MNMAPI, please refer to https://github.com/Lemma1/MAC-POSTS/tree/master/side_project/network_builder

Instructions

Please clone the whole repo, and run runner.ipynb using jupyter notebook.

Experiments

To check the details of the experiments in exp_config.py, please refer to the paper.

File specifications

  • src/exp_config.py: experiment settings in the paper
  • src/gp.py: gradient projection method
  • src/models.py: implementation of multi-modal DUE
  • src/runner.ipynb: script that runs the MMDUE
  • img/.: imagines used in the paper
  • data/input_files_small_multiclass: experiment network in the paper

For any question, please contact Wei Ma ( [email protected] ) and Xidong Pi ( [email protected] ).

  • Jupyter Notebook 99.6%
  • Python 0.4%

IMAGES

  1. Inputs and Outputs of Traffic Assignment

    traffic assignment source code

  2. Traffic Assignment Process.

    traffic assignment source code

  3. PPT

    traffic assignment source code

  4. PPT

    traffic assignment source code

  5. Mod 6, Part 1: Traffic Assignment (Introduction)

    traffic assignment source code

  6. Chapter 9: Traffic Assignment Model: Methods with examples

    traffic assignment source code

VIDEO

  1. Traffic Management

  2. TRAFFIC ENGINEERING ASSIGNMENT-2

  3. Traffic Assignment Part1 Dr. Ibrahim Ramadan

  4. Holiday Assignment project/ Traffic🚦 light signal craft# shorts

COMMENTS

  1. traffic-assignment · GitHub Topics · GitHub

    traffic-assignment Star Here are 21 public repositories matching this topic... Language: All Sort: Most stars vlarmet / cppRouting Star 97 Code Issues Pull requests Algorithms for Routing and Solving the Traffic Assignment Problem

  2. GitHub

    main README TAP101 A collection of good open-source projects covering the fundamental components related to the Traffic Assignment Problem (TAP). Shortest Paths tntp from Dr. Hillel Bar-Gera. One of the most efficient Deque implementations of the modified label correcting (MLC) algorithm in C. See mincostroutes.cpp for details.

  3. GitHub

    Source code for user equilibrium traffic assignment and models for scheduling road upgrades (see "Traffic Assignment and Road Network Upgrade Planning" slides for "Mesoscopic modelling of traffic networks" workshop). Imported from code archives available from https://sites.google.com/site/alexdstivala/home/traffic_models Reference

  4. PDF User equilibrium traffic assignment: k paths subtracting-adding algorithm

    Traffic assignment problem: Short literature review Campbell (1952) gave one of the first descriptions of the traffic assignment problem: "The ... - part of demand from the source node r to destination node s that use path p The following is the pseudo-code of the subtracting-adding procedure: Algorithm 2 - Subtracting-adding procedure Step 1

  5. traffic2.gms : Traffic Assignment Model

    Description In this traffic assignment problem, we have a number of users/cities, connected by a shared network of roads. Each user n needs to move a fixed quantity from her city n to city n++3 over the shared network. Each user can route flow along multiple paths between her source and destination nodes.

  6. Papers with Code

    In this study, we present a new macroscopic user equilibrium traffic assignment problem (UE-pTAP) framework for pedestrian networks while taking into account fundamental microscopic properties such as self-organization in bidirectional streams and stochastic walking travel times.

  7. traffic.gms : Traffic Assignment Model

    $title Traffic Assignment Model (TRAFFIC,SEQ=3) $ontext A simple traffic assignment problem: - Move 5 units of stuff from A to B as quickly as possible, using one or more of the N paths p1..pN between A and B.

  8. Dynamic Traffic Assignment for Electric Vehicles

    We initiate the study of dynamic traffic assignment for electrical vehicles addressing the specific challenges such as range limitations and the possibility of battery recharge at predefined charging locations. We pose the dynamic equilibrium problem within the deterministic queueing model of Vickrey and as our main result, we establish the ...

  9. What is the best free software for traffic assignment?

    1 Recommendation Popular answers (1) Bart Jourquin Université Catholique de Louvain - UCLouvain If your are working with Windows, you coud have a look at Tranus (...

  10. IJGI

    Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. ... Consequently, an increasing number of researchers are focusing on the integration of traffic assignment models with multi-source datasets [35,36,37,38], ... The record consists of the vehicle ID, site code, record time, and driving ...

  11. matteobettini/Traffic-Assignment-Frank-Wolfe-2021

    README Traffic-Assignment-Frank-Wolfe-2021 This simple script computes the traffic assignment using the Frank-Wolfe algorithm (FW) or the Method of successive averages (MSA). It can compute the User Equilibrium (UE) assignment or the System Optimal (SO) assignment.

  12. 7. Traffic Assignment

    7.1.1. Traffic Assignment Class. Traffic assignment is organized within a object new to version 0.6.1 that includes a small list of member variables which should be populated by the user, providing a complete specification of the assignment procedure: classes: List of objects Traffic class , each of which are a completely specified traffic ...

  13. Dynamic Traffic Assignment

    The Transportation Authority uses a Dynamic Traffic Assignment (DTA) model to give planners a more fine-grained view of transportation system performance and a better understanding of traffic patterns around San Francisco. ... Created an open-source code base to lower the barrier to entry to large-scale DTA modeling and reduce the amount of ...

  14. Traffic Assignment

    The fundamental aim of the traffic assignment process is to reproduce on the transportation system, the pattern of vehicular movements which would be observed when the travel demand represented by the trip matrix, or matrices ,to be assigned is satisfied. The major aims of traffic assignment procedures are:

  15. PDF A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi

    Accordingly, a quasi-dynamic traffic assignment model (DQ-DTA) based on multi-source data is proposed in this paper with the goal of achieving better assignment accuracy on large-scale expressway networks. This approach can exploit multi-source data fusion to address the shortcomings of STA models, and can approximate the effect of DTA models

  16. What is Traffic Assignment?

    Traffic assignment is a key element in the urban travel demand forecasting process. The traffic assignment model predicts the network flows that are associated with future planning scenarios, and generates estimates of the link travel times and related attributes that are the basis for benefits estimation and air quality impacts. The traffic ...

  17. Chapter 8 Assignment

    The source code things.cxx is available in the locations listed below. Your makefile should also include "all" and "clean" options--but there is no target for the Queue template class! Other files that you may find helpful: queue2.hqueue2.template: My implementation of the Queue class.

  18. traffic-assignment · GitHub Topics · GitHub

    traffic-assignment Star Here are 21 public repositories matching this topic... Language: All Sort: Recently updated jdlph / Path4GMNS Star 41 Code Issues Pull requests Discussions An open-source, cross-platform, lightweight, and fast Python path engine for networks encoded in GMNS.

  19. Route assignment

    Route assignment, route choice, or traffic assignment concerns the selection of routes (alternatively called paths) between origins and destinations in transportation networks. It is the fourth step in the conventional transportation forecasting model, following trip generation, trip distribution, and mode choice.

  20. Traffic Control Management System C++ Project

    The source code of Traffic Control Management System has been written in C++ programming language with the use of video graphics. You can download the source code, executable file and basic description of this project from the link provided in this post.

  21. Ashkanfld/Traffic-Assignment-Frank-Wolfe-2022

    The entire code is developed by Ashkan Fouladi and Vahid Noruzi based on python. - GitHub - Ashkanfld/Traffic-Assignment-Frank-Wolfe-2022: This python-based script computes the traffic assignment using the Frank-Wolfe (FW) method. The entire code is developed by Ashkan Fouladi and Vahid Noruzi based on python.

  22. Traffic Management System Using Java

    In this assignment you will finish building a simple simulation of a traffic management system (TMS). A traffic management system monitors traffic flow in a region and adjusts traffic signals to optimise traffic flow. A TMS uses different types of sensors and signals to monitor and manage traffic flow. In the first assignment you implemented ...

  23. Rantz: Seattle students told it's 'white supremacy' to love reading

    White students told to apologize in yet another Seattle high school controversy. Another aspect of the white supremacy lesson at this Seattle school involved a video titled "Getting Called Out ...

  24. A general formulation for multi-modal dynamic traffic assignment

    A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking - GitHub - Lemma1/Multimodal-DUE: A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking