Optimization of ITS for Urban TrafficRahul Kayala – 1RV10EC122

Nikhil Bharat – 1RV10ECO68

Contents

ITS

Highway Networks

City Networks

ITS for City Networks - Challenges

Intelligent Transport System

Control and Mitigate Traffic Congestion

Multiple Underlying Technologies – GSM, 802.11, Zigbee

Largely focused on Highway Networks

Safer, more Coordinated, and 'Smarter'

Long Uninterrupted Stretches

Reasonably Fast Moving Traffic

Service Roads for Slower Traffic

Longer Travel Times

Known Choke Points Highway

Networks

Challenges of ITS for City TrafficThe road links are short and the number of links is large – Computationally costly

Traffic flow is frequently split up due to the prevalent existence of intersections

The traffic management at intersections, e.g. traffic signal control, has strong impact on traffic flow pattern

The occurrence of traffic congestion is more accurately indicated by traffic volume on road links rather than travel speed.

Shorter Travel Time

Large Volume of Traffic

Unknown Choke Points

ITS TECHNOLOGIES

Driver assistance systems:

-Antilock Blocking System (ABS)

-Electronic Stability Control (ESC)

-Adaptive Cruise Control (ACC)

- Lane Departure Warning (LDW)

- Lane Change Assistant (LCA)

-Intelligent Speed Adaptation (ISA)

-Collision Avoidance System (CAS)

Automated Highway Systems:

-Cooperative Vehicle Infrastructure Systems (CVIS)

Commercial Vehicle Operations

Traffic Prediction for Urban Scenarios

Aim: Path Of Least Delay For Commuters Between A And B.

Most Important Application

Pre-Emptive – Road Network Design

Prevailing or Predictive – Existing Networks

Prevailing network conditions utilize historical data to analyse existing traffic conditions at the given time.

Predictive network conditions use historical data to predict the traffic conditions - advanced statistical techniques

Predictive Analysis

Parametric Techniques -- Historical Average and Smoothing Techniques, Regression, Kalman filters, Autoregressive Integrated Moving Average (ARIMA).

Non Parametric Techniques -- Regression, and Artificial Intelligence techniques such as Machine learning, Fuzzy logic and Neural networks.

Non Parametric

Pros Easier to Implement, Better Performance

Cons requires large amount of historical data, training processes, fails if good pattern match doesn’t exist.

TrafficModelling Assumptions

Discrete-Time Dynamic System

Rolling Horizon Approach.

Time Horizon is divided into discrete traffic prediction time intervals whose length is t seconds

Prediction is performed recursively every t seconds

Acceleration and Deceleration of the vehicles is neglected.

Model 1 – Uplink/Downlink Based Prediction

Traffic flow along the successive road links on a route

Traffic flow propagates from upstream road links to downstream road links in a traffic network

Split up at intersections

Model 1

Model 2 –Saturated Inflow

Inflow and outflow rate on the concerned link is largely determined by its spare capacity

Model 2

Model 2

Model 1 Results

Model 2 Results

Error Analysis

Error Reduction• MAE by 52% • MAPE by 36% • RMSE by 47%

Collision Avoidance

Safety is a prevalent issue in Intelligent Transport Systems (ITS).

The cooperative vehicles are handled by the controller for which we are seeking a collision avoidance

Strategy, whereas the uncontrolled ones are handled by the environment.

Configuration of the section is a tuple s = <d, A> where:

• d is an integer array indexed by the lanes such that d[i] denotes the time elapsed since the last entrance on lane i when d[i] < dmin and otherwise d[i] = dmin.

• A is a finite set of vehicles.

Environment Transition

Initial Configuration s = <d, A>

New state Configuration s1 = <d1, A1>

For every lane i, the environment performs d1[i] = min ( d[i] + 1, dmin).

If there is an uncontrollable vehicle a, its move is performed.

Controller Transition

Let s1 = <d1, A1> be a configuration

A new configuration s′ =<d′, A′> can be reached by a transition from the controller by moving every controllable vehicle of A1.

Assessment Analysis

ITS subject to a variety of conditions,

- maturity of technology

-market acceptance

-willingness to invest and buy.

Capacity Improvement

DAS technologies improve road capacity and reduce congestion through speed and headway adjustment.

Two ways of improving capacity-

reduces distances between fully automated vehicles until they reach the minimal safe distance.

stabilises the traffic flow. Traffic equilibrium can be reached avoiding stop-and-go operations and inefficiencies caused by inattentiveness, merging, weaving and lane changing.

CO2 emissions reduction

DAS applications such as ACC and ISA have a direct effect on CO2 emissions reduction through changes in speed.

ITS reduces CO2 emissions indirectly by alleviating the congestion caused by accidents.

ISA is expected to contribute to a reduction of fuel burn and CO2 emissions by 2–5%.

Conclusion

It was found that the performance of Model-1 peaks when the prediction interval is similar in order as the link travel time.

Model-2 demonstrates superiority when the prediction interval is larger than one minute.

Interoperability and standardisation issues need to be resolved to achieve large scale integration of vehicle and transport infrastructure.

References

[1] Zilu Liang and Yasushi Wakahara, "City Traffic Prediction based on Real-time Traffic Information for Intelligent Transport Systems," 13th International Conference on ITS Telecommunications (ITST), 2013.

[2] V. Psaraki, I. Pagoni, and A. Schafer, "Techno-economic assessment of the potential of intelligent transport systems to reduce CO2 emissions", IET Intelligent Transport Systems, pg 356-364, March-2012.

[3] D. Daily, F.W.Cathy, and S.Pumrin,” An algorithm to estimate mean traffic speed using uncalibrated cameras,” IEEE Conference for Intelligent Transportation Systems, 2000.

[4] L. Lee, R. Romano, and G. Stein,” monitoring activities from multiple streams: Establishing a common coordinate frame,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2000.

[5] Béatrice Bérard, Serge Haddad, Lom Messan Hillah, Fabrice Kordon and Yann Thierry-Mieg, " Collision Avoidance in Intelligent Transport Systems: towards an Application of Control Theory", 9th InternationalWorkshop on Discrete Event Systems, 2008.

[6] T. Kato, Y. Kim, T. Suzuki, and S. Okuma, “Model predictive control of traffic flow based on hybrid system modeling,” IEICE Trans. on Fundamentals, vol.E88-A, no.2, pp.549-560, 2005.

[7] Y.Wang, M.Papageorgiou, and A.Messmer, “Real-time freeway traffic state estimation based on extended Kalman filter: a case study,” Transportation Science, vol.41, no.2, p.167-181, 2007.