Animation and visualization of New Jersey traffic flow

Transcription

1 Animation and visualization of New Jersey traffic flow Tharald Sund Fongaard & Ed Walker Final Project ORF 467 Transportation Systems Analysis. Fall Semester 2013/2014 Tuesday January 14 th 2013 Princeton University, NJ USA [Princeton University]

2 ANIMATION AND VISUALIZATION OF NEW JERSEY TRAFFIC FLOW Table of Contents Introduction Page 3 Building the network model Page 4 Building the congestion animation model Page 5 Pixel maps Page 7 WebGL Page 7 Results and discussion Page 7 Associated data Authors: Tharald Sund Fongaard Undergraduate Student 4256 Frist Ctr fongaard@princeton.edu Ed Walker Undergraduate Student 1221 Frist Ctr eswalker@princeton.edu Group: Fongaard, Tharald Sund Walker, Ed NetIDs: Fongaard Eswalker This paper represents our own work in accordance with university regulations. Tharald Sund Fongaard (Digital Sign) Ed Walker (Digital Sign) 2

3 Introduction In this project we will take a look at the traffic flow of synthesized trips in New Jersey for a 24- hour period. The trip data was provided to us, together with network data in the form of road names and locations. We have used this data to build a network capable of several simulations. Using optimizations and animation tools we were able to create several interesting models. A well- presented and lucid model can play a very important role when it comes to planning and optimizing network upgrades. In our case the model was created to calculate demand for autonomous taxis. Investors looking into starting operations for autonomous taxis would be concerned with applying the most cost effective and profit maximizing solution. It is therefore extremely important that the engineers that create these models use realistic and coherent data. An optimal model is extremely valuable for all parties included in the production chain both at investment decision, development and production phases of the autonomous taxi. There are many specialized tools for network simulation, flow simulation in peak hours and traffic assignment etc., but very few are well integrated with economic modeling for autonomous taxi operations. None of these tools are made to animate synthesized traffic data for autonomous taxi demand. The approach to create these types of animation tools can vary, and it is usually up to the engineers that create them to choose the algorithms that create the best fit. In the end, the models that are made are approximations of the real behavior (in this case synthesized). As mentioned above, it is in the engineers interest to produce a model that displays lucid and usable visualizations. In our case, we used synthesized data from all the counties in New Jersey to create the animations. The data in this assessment is a combination of publicly available network data and class specific synthesized trip data. The data was provided to us by Prof. Alain Kornhauser (Princeton University) and Mike Bodden (Senior Vice President of Technology at ALK Technologies) Our approach for this project was: Create a fast and reliable network model based on the network data provided to us. Using the above model, create animation specific data using synthesized trip files as a input Using the above animation specific trip data; create a generic interface that runs in any browser capable of animating and simulating traffic flow without installing any programs and without relying on any plug- ins The goals of the data visualization project are to provide a visual geographic representation of our trip data. The project aims to work at scale with a large number of trips, visualize trends to validate the accuracy and reasonableness of our data, and test the viability of trip animations using different animation APIS such as WebGL and Angular. 3

4 ANIMATION AND VISUALIZATION OF NEW JERSEY TRAFFIC FLOW Building the network model The network model is a java based simulation tool based on the datasets for network links and nodes. (See attached files nodefile.txt and linkfile.txt). The java implementation uses the above data to create several parallel data structures. The interface creates four Separate Chaining Hash Tables which maps nodeids to Point2D objects and vice versa as well as Directed Edge objects to Links and Link numbers to Links, this allows the interface to grab data in almost instant time. The interface also creates a Kd- Tree containing all the nodes, to be able to find the entry node for any given trip in linear time. The main object is an Edge Weighted Digraph, which allows Dijkstra algorithms to be run on it. See attached program and incorporated comments for more detailed description. Using the above data structures, the network model pulls data from datasets like otripfiles and generates trips as path objects. The network implementation allows the user to output animation specific data in a flexible manner. Eg, link to link data or link entry times. The program runs fairly quickly, but writing huge datasets (eg. Essex on over 950MB) takes time. 4 With the above described program and its associated support programs we were able to create.json files containing link entry times and paths as link to link arrays. To the left is a list of the data files created and their sizes. We created four sets of data; taxi, train, walk and intra- pixel. Each file is a JSON type file, which allows it to be read in as a data structure in JavaScript. The files have a field ID, which represents a linear combination of the originating county and the person ID. This process is done with cantor pairing function. π N N N π k!, k! = (k! + k! )(k! + k! + 1) 2 + k! The most important property of the function is that it is reversible. Allowing decoding of the number at a later stage. The above generated number is used as the base and is separated with trip index by a period, giving the following format on the trip ID (the first field in the json files): (Cantor(county,person)).index e.g The JSON files then has departure time, to allow sorting for displaying specific time periods. The remaining fields are linkid numbers, which represents the trip that that person is taking. The cantor number is reversible, and so it is a useful compression step in the data files, and allows indexing with IDs to be quite streamlined. We also generated parallel time files, which contain the entry time for each link. These files were used for the WebGL animation part of the project. To the right is our java library for the network model. The main faunctions are CreateNetwork.java and ReadOtrip.java. These programs are used to parse data as described above. See associated data files for more specific information.

5 Building the congestion animation model The app is live here: To begin the assessment of this part of the project we had to look at some of the constraints in our model, and what we wanted the final output to be. We had to decide what platforms to develop the animation, and whether we wanted to make it data specific or generic. We landed on a midway, developing an animation tool that was going to be able to run in any browser on any operating system, and dependent only on the data files provided to it, no hardcoded animation data. This would allow the potential user to quickly change between datasets, and also for the super user to change the datasets in the future, if found necessary. To achieve the above goal we chose to use JavaScript on an HTML platform as our animation tool. JS has a lot of drawing and animation libraries available. For this part of the project we landed on the Google Map API with browser scripts Angular, Bower and Yeoman. The model is built up with tidiness in focus, making it extremely generic. Angular allows the programmer to separate data, javascript, controllers and HTML while maintaining the speed and intractability on the web interface. Building a web app requires a lot more framework than building java libraries, as user data is provided in so many different fields, and in any order, compared to a java program. To the left, the overall structure of the web app is displayed. As you can see, the overall structure allows for quick change and formatting on any part of the interface, including the data. A very important optimization step that was necessary for the congestion model to run fast enough was to compress the huge link files so that the browser could load and display them without users screaming at the screen. To do this, we opted to use PERL as it has good capabilities when it comes to reading and working with datasets and it can be run quite easily from shell scripts. The PERL script can be found in the associated data. The script filtrates the huge data sets, which contains every single trip and all its links and creates a dataset which contains only the links that should be drawn, together with time filtration. This was a huge optimization allowing data compression up to 1/500. For example, animation data for Essex alone is abut 950MB, with the above compression the resulting data is only 2.2MB! The flow model selects datasets based on user input by using format strings, sorts the data according to time interval and draws congestion levels with the following function: number of accesses 1 strokeweight = 10 total accesses The above function allows the least used links to be drawn even if there are some very heavy congested roads in the calculation. The above function is relative which we opted to include in the drawing process. This makes congestion on links relative to the current dataset being drawn, not relative to the most congested link of all the data files in all time intervals. This design decision was 5

6 ANIMATION AND VISUALIZATION OF NEW JERSEY TRAFFIC FLOW made to make the animation illustrative of the congestion levels in the selected timeframe. This is important to notice when comparing different data sets and time frames. Stroke widths are not comparable across data sets. The resulting flow charts are very realistic, and displays traffic flow going out of the selected county. To the right is a cut out of the Atlantic county taxi trips in the time frame 1am 12pm. As one can see, the Dijkstra algorithm has selected the fastest route for all trips, resulting in a flow chart heavily weighted on the Atlantic City Expressway, as a lot of the trips are going into Philadelphia. Below is comparison between Google Maps and our dijkstra algorithm. The image illustrates the correctness of our dijkstra implemention. Our algorithm selects roads based on time, not distance. Using the same parameters in Google yields almost the exact same result. The only difference is where Google choses to take west by Newark, probably to avoid traffic or road work. Our algorithm does not account for traffic and road work, and is also based off a different network data set, which might contain different information. The result is however almost exactly the same. 6

7 Pixel maps A public web interface for creating NJ pixel maps is available at The pixel maps project shows trip destination densities from the otrip files. Darker pixels have more trips that end in that pixel. For 1% of trips, the map shows a white line from the starting pixel to the ending pixel. Trips can be filtered by county or time of day. At right is a pixel map of 32 million person- trips. The pixel maps are generated by a Java program. The web interface is written in html and css. A server- side php script invokes the java app. The java apps reads the relevant county otrip files and generates a map. Map creation can take a few seconds, so all generated maps are cached. WebGL Animation A demo animation of a subset of trips originating from Atlantic County is available at The application uses data from a digital map database and Dijkstra's shortest path algorithm to route trips through the NJ road network. The main logic for the application is written in javascript. At right and below is a screenshot of the webgl demo animation. Results and Discussion The data we have generated can be used to produce a series of different output. We have chosen to animate a few of what we consider the essentials. The programs and animations can be expanded on in the future both for potential junior independent work and a senior thesis topic. Overall, these images and animation of the data provide a toolset to quickly investigate trends, assumptions, and inconsistencies in our data. As we created the tools and looked at the data using them, we found results that looked reasonable and for the most part consistent with our common- sense predictions of person- movement. These positive results suggest a fairly accurate person- trip model. 7