Speed-Flow Modeling of Arterial Roads in Singapore

Lum, Kit Meng; Fan, Henry; Lam, Soi-Hoi; Olszewski, Piotr

doi:10.1061/(asce)0733-947x(1998)124:3(213)

Cited by 44 publications

(28 citation statements)

References 2 publications

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“…Figure 2 shows the traffic flow Q as a function of traffic demand D. The capacity of the road (including all signalized intersections) can be defined as the limit value of Q for high D. It can be seen that, for most part, capacity depends on the green split, and that installing the green wave slightly increases the road capacity, by about 6 %. This is somewhat lower than what is found empirically (e.g., Lum et al (1998) found an increase of about 8.4 %), which is probably due to the simple layout and limited size of the simulated network. Because of the small influence of the cycle time, the results shown in Figure 2 are averaged over the different cycle times for the purpose of clarity.…”

Section: Road Capacity and Travel Timescontrasting

confidence: 73%

Effects of traffic signal coordination on noise and air pollutant emissions

Coensel

Can

Degraeuwe

et al. 2012

Environmental Modelling & Software

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Traffic management solutions are increasingly called for to address problems of transport and mobility. In particular, coordinated traffic lights that create green waves along major arterials are an increasingly used strategy to reduce travel times. Although it is usually assumed that an improved traffic flow will result in lower vehicle emissions, little scientific research has been spent on the effects of synchronized traffic lights on emissions. Moreover, because changes in traffic flow do not necessarily influence travel times, noise and air quality in the same way, there is a clear need for a combined approach. This paper reports on a computational study in which a microscopic traffic simulation model (Paramics) is combined with submodels for the emission of noise (Imagine) and air pollutants (VERSIT+). Through the simulation of a range of scenarios, the model is used to investigate the influence of traffic intensity, signal coordination schemes and signal parameters on the noise, carbon dioxide, nitrogen oxides and particulate matter emissions along an arterial road equiped with a series of traffic lights. It was found that the introduction of a green wave could potentially lower the emissions of the considered air pollutants by 10 % to 40 % in the most favorable conditions, depending on traffic flow and signal timing settings. Sound pressure levels were found to decrease by up to 1 dB(A) near the traffic signals, but to increase by up to 1.5 dB(A) in between intersections. Traffic intensity and green split were found to have the largest influence on emissions, while the cycle time did not have a significant influence on emissions.

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Section: Road Capacity and Travel Timescontrasting

confidence: 73%

Effects of traffic signal coordination on noise and air pollutant emissions

Coensel

Can

Degraeuwe

et al. 2012

Environmental Modelling & Software

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“…[29] showed that the average speed depends on the road's geometry, on the trac ow characteristics and on the trac signal coordination. A new travel time-density model was formulated by incorporating the minimum-delay per intersection and the frequency of intersections as parameters.…”

Section: Factors That Inuence the Estimation Of The Travel Timementioning

confidence: 99%

“…The traveling time and the trac volume are two main eld items that have to be considered for the speed-ow study along arterial roads. The traveling time can be observed in two ways [29]:…”

Section: Factors That Inuence the Estimation Of The Travel Timementioning

confidence: 99%

Personal Advanced Traveler Assistant

Radu¹

2011

Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics

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In spite of numerous road management schemes and developed infrastructure the society nowadays still faces the problem of highly congested roads due to the increasing trac demand.The focus of this thesis is to develop a complex and integrated system that addresses the challenges of dynamic trac assignment in modern times. To reach this goal we had to study the theory behind such systems, come with new ideas and implement them into a real world human-centered traveler information system. Taking into account state of the art systems in this eld we built a design for a Personal Advanced Traveler Assistant (PTA). The main purpose of PTA is to give routing advices depending on the users preferences and the available capacity in the network. The system incorporates a dynamic trac algorithm that employs a prediction model. The prediction model should use current trac updates and the routes of the guided cars in order to achieve an accurate prediction of the future traveling times.Next step was to implement a prototype. This is available through multiple interfaces like smart phones or desktop applications.The prediction model that the algorithm uses is based on historical data. Given the time constraints the prototype uses now only historical data but it can be easily extended to include live updates. This allowed us to build a prediction model that considers typical trac dynamics in order to estimate the traveling time.The performance measure of the trac assignment algorithm is the shortest traveling time. The algorithm that we implemented is a time dimensional-extended version of the Dijkstra algorithm. The algorithm is tested on real data collected from the highways in The Netherlands. We evaluate the performance of the algorithm by comparing it to two versions of the static Dijkstra algorithm.To conclude, we successfully implemented a working prototype that uses various technologies such as Java, the Open Street Map API for rendering the map or J2ME for the mobile phone client. The prototype that we have built represents a working proof of concept for a dynamic routing assistant. One advantage of the structure that we chose to implement is that each component can be further on extended independently. In this way we showed that such a system is feasible but we also left the possibility for dierent parts of the system to be extended into more advanced applications. The master thesis that you are reading represents my graduation project as a Master student at Delft University of Technology. I would like to take this opportunity and thank those who helped and supported me during the last 2 years. I thank especially to Prof. L.J.M. Rothkrantz for guiding my steps to successfully accomplish this master thesis. I am grateful to him for all the support he oered me, the eorts he invested in this research project, for giving me new ideas whenever I though I reached a dead end and not only. I would also like to thank him for trusting me and for making me feel more condent. I consider myself lucky to have met him and e...

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“…When it comes to the factors affecting travel time, literature review identifies free flow travel speed, occurrence of incident situations, holidays or other uncommon events, congestion level and weather conditions [17,18].…”

Section: Introductionmentioning

confidence: 99%