7.1.1 Real Time Traffic

Android Application is based on Real-time traffic data engine using advanced cloud APIs, IOT, Machine learning, and cloud computing [1–5]. It includes crowdsourcing data concept also to collect the user’s location to enhance the information accuracy from time to time. In backend APIs we have used TensorFlow advance methods to train our model for giving results effectively.

It consists of all updated technology on android environment because this is really flexible for users.

7.1.2 Traffic Simulation

Traffic diversion or the reenactment of transportation structures is the logical showing of transportation systems (e.g., interstate convergences, vein courses, roundabouts, downtown grid structures, etc.) through the usage of program to all the more probable help plan, structure, and work transportation frameworks. Reproduction of transportation structures started indeed forty years prior, and is a critical locale of request in busy time gridlock building and transportation masterminding today. Distinctive national and close by transportation workplaces, insightful associations and directing firms use reenactment to help in their organization of transportation frameworks [5].

This chapter explains the android application project based on real-time traffic information engine. It is based on IoT, ML, MVVM, cloud computing, microservices, and JSON parsing. It will explain the whole workflow, architecture, and model of android application. Real life use cases and scenarios where it can help users to get the real time information and how they are contributing to make this better are also discussed thoroughly in this chapter.

The backend workflow of machine learning and how it will improve real-time data rendering engine from time to time is also explained briefly.

Here a combination of different new technologies is used to tackle the problem of real time traffic information. To understand the whole working model, one should first understand how traffic is being calculated.

7.4.1 API Architecture

We are using cloud computing and google traffic channeling API for getting data and using FLY-CROW to make more accurate with cognitive learning models as shown in Figure 7.2.

As per the architecture or request we are using SSL offloading to make it more secure and routing for service resources. We are using logger to log all the operations whatever is happening. It will help to debug the issue at the time of emergency.

Response caching helps to make application more fast at the time of giving response of same request data [14].

7.4.2 File Structure

It is important to divide basic file structure to access the correct file at appropriate executable time as shown in Figure 7.3.

GPS Enabled Architecture—As we all know crowdsourcing data process in which we are getting data from many users and tell results on the basis of the data but in traffic analysis case we are using data from user’s device and GPS satellites both. Here the accuracy depends on the number of active satellites on specific location.

We can understand this by taking one example:

Let’s say we are going from Location A to Location B with surrounding of n no. of users having GPS enabled in their device so the data what we will get as a traffic on our devices it depends on number of users, their vehicle speeds, and GPS data given by number of active satellites on specific location. One more important thing is required, the latitude and longitude of specific location because without this API can’t able to give specific outputs for different locations.

ML plays an important role in this process because with each operation it is getting smarter to provide more accurate output for users [14].

Why AWS IoT—There are such a large number of administrations expansive and profound, multi-layered security, unrivaled AI incorporation, and demonstrated at scale. There are some different things which API has based on AWS IoT center, AWS IoT Device Defender, AWS IoT Device Management, AWS IoT Things Graph, AWS IoT Analytics, AWS IoT Events, and AWS IoT Sitewise [15].

7.4.3 Simulator Architecture

Parameterized Turning Speed—The speed at which turns are made ought to be tunable by the client or variable subject to turning range, number of ways, and so on. It is possible that the turning-speed model could influence computation of surrogate measures. SIMTRAFFIC claims a parameterized turning-speed model and VISSIM and Texas licenses going rate to be subject to vehicle type and turning range [16].

Response to Yellow—Appearing of a driver’s response to yellow is major to quantify issue zone execution. It could be colossal for computation of surrogate measures if the response model is variable by driver type, vehicle type, and so forth. Most models explored have response “by driver type.” Paramics records its showing limits “by driver.” This assembles a steady size of parameters, as opposed to a lot of fixed parameters (one for each sort) [16]. VISSIM has response models with unequivocal driver-type parameter settings for both sign groupings with and without blasting completions administrative work (for both European and North American signalization moves close).

Variable Driver Reaction Time—Mirrors the model’s capacity to address the concede experienced between the driver’s particular confirmation of a potential mishap and the utilization of control measures (slowing down, accelerating, or route change) to stay away from influence. When in doubt, drivers’ response times fluctuate by getting, age, and so on. HUTSIM is planning coordination of nanoscopic appearing of driver responses and Paramics models driver care.

Intersection point Box Movements- For assessment of surrogate flourishing measures, it is colossal for the reenactment to show headway of the vehicles in the convergence point with fundamental commitment. For instance, for left turns, Texas models get together progressions as blends of properly evaluated round parts from the purpose of intermingling of the starting route to the point of assembly of the getting way

Variable Acceleration (and Deceleration) Rate—Diversions should join displaying of various vehicle constrains by vehicle type. Unbelievable DRs (and most remarkable DR distributions) may deride the genuine encounters of surrogate measures. This is related with all models that were watched out for.

Sight-Distance Limits—Models that limit the “look-ahead” parcel of drivers when picking (or model the look-ahead separation by driver or driver type) can much more precisely model the shared trait with drivers in surrogate measure encounters. Additionally, sight-separation cutoff centers can mirror the displaying of roadway counteractions, for example, turns, tops, trees, structures, and so forth. This may also apply to appearing of in-vehicle sight imperatives, for example, those that happen when following a huge truck. A gigantic piece of the models outlined need refined sight-segment hindrance showing. VISSIM makes them model of both the measure of vehicles to look forward and a division ahead to consider before making moves (as do different models as recorded in the table), yet no block impacts are appeared [16]. CORSIM has a sight-division limit for vehicles at the stop bar to examine ahead for vehicles clashing with their improvement in the crossing point.

Moving Yield—Cautious displaying of posted notification and zones will be basic for precise gathering of surrogate measures. It is guessed that the SSAM will be utilized for flourishing assessments of yield assignments versus stop or signalized works out [16]. A “moving” yield displays that the yield development can happen with a moved back vehicle that doesn’t appear at a stop before returning the traffic stream.

Vehicles Interact With Pedestrians—Individual by strolling success is of over the top centrality to traffic engineers. Redirections that model vehicle correspondences with walkers may have the choice to review the person by strolling security impacts of different various decisions. VISSIM and Paramics expressly model walker enhancements in crosswalks during individual by strolling timings.

All around masterminded Merging—Suggests the marvel where certain driver types moderate or stop to permit vehicles to blend (more) securely, which happens when in doubt, instead of just demonstrating moving back or halting from a responsive perspective. Neighborly joining shows that the going with vehicle can make a hole for a joining vehicle. CORSIM and VISSIM join such lead and AIMSUN unites such direct for incline assemblies.

Appearing of Multilane Merging Behavior—In different locales, it is run of the mill for vehicles entering the mainline stream to cross the strategy for an advancing toward vehicle going a practically identical course as the masterminded bearing of advancement of the entering vehicle and begin charging in the circumscribing way. As of now, advancing toward vehicle can proceed at its current speed without breaking for the turning vehicle (the move is viewed as obliging conduct). Expansion models that consider such practices to happen will considerably more unequivocally address the debate direct of territories that experience high volumes of such practices with wide multilane arterials [17]. VISSIM can show such direct with upheld passages for unequivocal driver types, yet it isn’t reliant on the way that the pushing toward vehicle is in.

Appearing of Right-of-Way in Intersection—A fundamental issue for demonstrating trouble occasions is that some turning practices must make slowing down occasions by the traffic that has the decision to proceed (i.e., making a left turn before advancing toward traffic) to be viewed as risky occasions. On the off chance that an amusement model doesn’t address this direct, the surrogates can’t be sensibly evaluated [17]. For instance, AIMSUN registers the TTC around the start of a left-go move to pick whether an opening can be perceived with sensible slowing down by the choice to proceed with vehicle. Along these lines, some initial attestation moves will, by definition, produce strife occasions.

Representing/Recording of Maneuver Failures—Certification of an opening is one occasion that can cause struggle occasions. Then again, the “dismissal of hole” occasions may also have a surrogate security proposal. Models that can record the removal or “disappointment” of the initial assertion technique could pass on another surrogate degree of the dispersing and number of dismissed holes. Models that can trade hole insistence occasion subtleties could in like way reasonably pass on hole dissatisfaction occasion subtleties. For instance, Texas can trade a table of contention “check” (normally an ejection if not followed by a certification occasion) and confirmation occasions.

Halting Maneuvers—On-road ending (proportional and twofold stopping) makes struggle conditions, way changes, and so on when in doubt and has a basic success impact. Reenactments that model on-road ending moves are delighted in. CORSIM models leaving as “emotionally happening on-road scenes of variable term,” rather than unequivocally indicating genuine vehicles halting to stop and a brief timeframe later restarting their excursion later. The mean term of ending occasions must be under 100 s, and there must be in excess of 14 occasions for consistently.

Displaying of Turn Signaling—One essential bit of back fight occasions is the utilization of signs by drivers. How flags (i.e., nonattendance of hailing) sway the vehicle following and way changing strategies for thinking is fundamental to investigating the rehash and truth of back fight occasions. Turn hailing is strikingly a badly arranged indicating wonder. AIMSUN, for instance, models the “crisis” of a vehicle changing to another way ahead of schedule of a go to pick how solid the vehicle will be in cutting off decision to proceed with vehicles to make its turn, which could be viewed as a sort of evident appearing of turn hailing. VISSIM models signals for way changes (i.e., signals are constantly utilized and two or three drivers will open holes to permit hardening), in any case doesn’t show the nearness or nonappearance of a sign at an advantage or left turn at an intersection point. Also, the vicinity of a sign on a vehicle in a circumscribing way impacts driver lead.

U-Turns—U-turns some of the time cause fight conditions and a few regions experience sufficiently high volumes of U-turn traffic that their effect on success ought to be tended to (e.g., including U-goes to relationship at the mix corner or to locate a decent pace entrance.

Minute vehicle lead and correspondence with the close to vehicles pick generally speaking traffic direct at the typically indisputable level subject to the going with segments: most unmistakable accelerating/deceleration and driver practices, for example, upheld headway and reaction time, hole confirmation edge for way changing, and shrewdness advance timespan or parcel for way creating [17]. Those parameters genuinely sway thickness and postponements in the engendering, and subsequently the general traffic structure. Underneath, we talk about the two elective models and feature the rule highlights.

State Route (SR) 99 northbound was picked for model change. This zone of interstate scopes from the Elk Grove Blvd. trade to the US-50 interstate exchange south of downtown Sacramento, CA. There are 9 exchanges with near to vein boulevards; 4 divided cloverleaf trades, 3 full cloverleaf exchanges, and 2 important stone exchanges with the region arterials. The entrance ramp hardening and weaving districts orchestrated at the Sheldon Rd. exchange, the Florin Rd. trade, likewise as the exit ramp at the US-50 road exchange, add to the morning top unpredictable postpone saw right now. This summit period usually starts at 6:30 AM and finishes around 10:00 AM, and the morning blockage arrangement shows the normal top time allotment when there is notoriety for suburb to downtown excursions during the morning hours [17].

As shown in Figure 7.4 is the carpool way information was amassed with those of the broadly significant routes considering the way that the carpool way is nearly plugged up and shows directly around a relative blockage design (bottleneck an area and length) as the for the most part accommodating ways. It may be seen that taking everything into account, the reproduced streams fulfilled the alteration measures. In any case, the AIMSUN model can’t actually reenact the development of the downstream weaving bottleneck at US-50, under the GEH, MAPE, and RMSE measures. This could be credited to the restriction that model’s way changing defense doesn’t mirror the curiously solid and visit way change direct. Indicating such zone may require better cleared a path changing opening certification standards that unequivocally fit such an exit ramp bottleneck [17].

Traffic reenactment models can give a financially savvy and adaptable choice to assess the different roadway plans and traffic the executive’s choices. To do as such, we first need to guarantee that the models are suitably aligned to mirror the current conditions. The utilization of unmanned elevated vehicle (UAV) has increased specific consideration in the transportation framework industry. On an across the country scale, there has been a pattern to join UAV in rush hour gridlock tasks, for example, traffic observing and framework the board exercises which incorporate extension and roadway assessments. The objective of this examination is to assess the viability of using UAV to approve traffic recreation model yields. Keeping that in mind, two reproduction models of a convergence in Auburn.

7.4.4 Workflow in Application

The activity diagram as shown in Figure 7.5 depicts the workflow in the application, when a user installs the application, GPS is enabled and then traffic data from Google server is requested map API is invoked and that particular response is received by the device and after rendering the data is received by the application.

7.4.5 Workflow of Google APIs in the Application

The pictorial representation of the workflow of APIs in the application and only depiction of the relation between the Google APIs in the application as shown in Figure 7.6.

7.5.1 Traffic Scenario

The traffic scenario is presented in the conventional traffic display methods i.e. using heatmaps.

7.5.1.2 Moderate Traffic

The moderate traffic scenario is the yellow color of the route predicts that there is moderate traffic on that route as shown in Figure 7.8.

Layers provides a good number of layering factors on the map structure to differentiate between the roads, highways and other concrete structures. The best thing the color effects are precise and are sequentially connecting themselves. The moderate traffic is always considered as the most less accurate traffic scenario but in this application usage of accelerometer makes it much more precise.

7.5.2 Speed Viewer

As shown in Figure 7.10 it shows speed screen with max speed, average speed, distance, time, and accuracy. The use of chronometer provides much more accurate speed detection and hence provide concrete data for traffic prediction. The speed is also considered as a proof for showing the applications accuracy for finding the speed and using it at the back end for finding the traffic scenarios.

7.5.3 Traffic Simulator

The simulator provided the flexibility to construct own passages and add/delete vehicles.

7.5.3.1 1st View

The simulator shows the traffic into 3 structural views, as shown in Figure 7.11 it shows the 1st view with edit mode turned on to add or remove cars. This could help the administration with various traffic scenarios.