fundamental diagram
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2022 ◽  
Author(s):  
Daniel Bramich ◽  
Monica Menendez ◽  
Lukas Ambühl

<div>Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10,150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question ``Which model for the functional form of an empirical fundamental diagram is currently the best?''. The word ``currently'' in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms.</div><div><br></div><div>Accepted by IEEE Transactions On Intelligent Transportation Systems on 14th Dec 2021<br></div><br>


Author(s):  
Aledia Bilali ◽  
Ulrich Fastenrath ◽  
Klaus Bogenberger

Ride pooling services are considered as a customer-centric mode of transportation, but, at the same time, an environmentally friendly one, because of the expected positive impacts on traffic congestion. This paper presents an analytical model that can estimate the traffic impacts of ride pooling on a city by using a previously developed shareability model, which captures the percentage of shared trips in an area, and the existence of a macroscopic fundamental diagram for the network of consideration. Moreover, the analytical model presented also investigates the impact that improving the average velocity of a city has on further increasing the percentage of shared trips in an operation area. The model is validated by means of microscopic traffic simulations for a ride pooling service operating in the city of Munich, Germany, where private vehicle trips are substituted with pooled vehicle trips for different penetration rates of the service. The results show that the average velocity in the city can be increased by up to 20% for the scenario when all private vehicle trips are substituted with pooled vehicle trips; however, the improvement is lower for smaller penetration rates of ride pooling. The operators and cities can use this study to quickly estimate the traffic impacts of introducing a ride pooling service in a certain area and for a certain set of service quality parameters.


Author(s):  
Zeyu Shi ◽  
Yangzhou Chen ◽  
Jingyuan Zhan ◽  
Xiangyu Guo ◽  
Shuke An

To describe the dynamics of traffic flow in the urban link accurately, the waves which generate at intersections are adopted as the influencing factors of traffic flow. Based on the urban traffic waves, a wave-oriented variable cell transmission model (WVCTM) is proposed to illustrate the urban traffic flow. In this model, the average density and length are the state variables. The cells are divided by traffic waves. The upstream cell is the influence area of the waves at the upstream intersection, the downstream cell is the influence area of the waves at the downstream intersection, and the rest is the mediate cell. Consistent with the fundamental diagram and the cell division, the traffic states of urban links are divided into six modes. The variation of modes is explained by hybrid automata. Finally, an experiment is designed to verify the feasibility of WVCTM. The data in the experiment come from the actual scene. Compared with the cell transmission model (CTM) and variable-length CTM (VCTM), WVCTM possesses the valuable performance to predict the traffic states. Likewise, it is rational that WVCTM can correctly illustrate the urban traffic flow.


2021 ◽  
Vol 118 (50) ◽  
pp. e2107827118 ◽  
Author(s):  
Daniel R. Parisi ◽  
Alan G. Sartorio ◽  
Joaquín R. Colonnello ◽  
Angel Garcimartín ◽  
Luis A. Pugnaloni ◽  
...  

We characterize the dynamics of runners in the famous “Running of the Bulls” Festival by computing the individual and global velocities and densities, as well as the crowd pressure. In contrast with all previously studied pedestrian systems, we unveil a unique regime in which speed increases with density that can be understood in terms of a time-dependent desired velocity of the runners. Also, we discover the existence of an inaccessible region in the speed–density state diagram that is explained by falls of runners. With all these ingredients, we propose a generalization of the pedestrian fundamental diagram for a scenario in which people with different desired speeds coexist.


2021 ◽  
Vol 101 ◽  
pp. 103090
Author(s):  
Emmanouil Barmpounakis ◽  
Martí Montesinos-Ferrer ◽  
Eric J. Gonzales ◽  
Nikolas Geroliminis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ziwen Song ◽  
Feng Sun ◽  
Rongji Zhang ◽  
Yingcui Du ◽  
Chenchen Li

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.


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