Modeling adaptive reversible lanes: A cellular automata approach

PLOS ONE RESEARCH ARTICLE Modeling adaptive reversible lanes: A cellular automata approach Dante Pérez-Méndez ID1,2*, Carlos Gershenson ID1,2,3, Marı́a Elena Lárraga4, José L. Mateos2,5 1 Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad de México, México, 2 Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México, 3 Lakeside Labs GmbH, Klagenfurt am, Wörthersee, Austria, 4 Instituto de Ingenierı́a, Universidad Nacional Autónoma de México, Ciudad de México, México, 5 Instituto de Fı́sica, Universidad Nacional Autónoma de México, Ciudad de México, México a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Pérez-Méndez D, Gershenson C, Lárraga ME, Mateos JL (2021) Modeling adaptive reversible lanes: A cellular automata approach. PLoS ONE 16(1): e0244326. https://doi.org/ 10.1371/journal.pone.0244326 Editor: Feng Chen, Tongii University, CHINA Received: September 4, 2020 Accepted: December 7, 2020 Published: January 4, 2021 Copyright: © 2021 Pérez-Méndez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data underlying the results presented in the study are available from Transport Infrastructure Ireland at https:// web.nra.ie/CurrentTrafficCounterData/index.html. Funding: This paper was support by Consejo Nacional de Ciencia y Tecnologı́a, México, http:// www.conacyt.gob.mx, grant number 630617, recipient D.P., PAPIIT UNAM(IN116220), recipient J.L.M., PAPIIT UNAM(IN107919) and PAPIIT UNAM(IV100120), recipient C.G. No additional external funding was received for this study. The funders had no role in study design, data collection * Abstract Dealing with traffic congestion is one of the most pressing challenges for cities. Transport authorities have implemented several strategies to reduce traffic jams with varying degrees of success. The use of reversible lanes is a common approach to improve traffic congestion during rush hours. A reversible lane can change its direction during a time interval to the more congested direction. This strategy can improve traffic congestion in specific scenarios. Most reversible lanes in urban roads are fixed in time and number; however, traffic patterns in cities are highly variable and unpredictable due to this phenomenon’s complex nature. Therefore, reversible lanes may not improve traffic flow under certain circumstances; moreover, they could worsen it because of traffic fluctuations. In this paper, we use cellular automata to model adaptive reversible lanes(aka dynamic reversible lanes). Adaptive reversible lanes can change their direction using real-time information to respond to traffic demand fluctuations. Using real traffic data, our model shows that adaptive reversible lanes can improve traffic flow up to 40% compared to conventional reversible lanes. Our results show that there are significant fluctuations in traffic flow even during rush hours, and thus cities would benefit from implementing adaptive reversible lanes. Introduction One of the major challenges in modern cities is to reduce traffic congestion [1, 2]. Improving public transport and promoting the use of alternative vehicles such as bikes and scooters is critical to reducing the number of cars on the streets. In addition to the above mentioned, we can also improve traffic congestion by implementing smart routing strategies, smart traffic lights, or changes in the infrastructure, such as reversible lanes. Far from conflicting, these strategies are complementary to improve traffic congestion. In the 1920s, some cities in the United States started to implement reversible lanes to deal with increasing traffic [3]. The reversible lane approach lies in this assumption: During rush hours, traffic flow in some twoway streets increases in one direction with respect to the other direction. In most cities, it is PLOS ONE | https://doi.org/10.1371/journal.pone.0244326 January 4, 2021 1 / 16 PLOS ONE and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Modeling adaptive reversible lanes: A cellular automata approach expected that early in the morning, people living in the suburbs commute to their workplaces in the center of the city and return home in the afternoon. This behavior creates an asymmetric demand in the roads that connect the city’s border with the center. Using this insight, city planners can decide to change a lane to a more demanding direction in a specific time interval [4]. As a result, the road’s capacity is increased in the direction with more traffic flow but reduced in the opposite direction. The reversible lanes are an easy way to increase road capacity without adding new infrastructure. However, they require exceptional planning and operation to have adequate performance. The existing methods for setting up a reversible lane are based in the observation that in most cases, there are two distinct peaks on the traffic demand, one in the morning and one in the afternoon. Based on these patterns, traffic managers decide to change the reversible lanes’ direction for a fixed time [5]. It could be one or several hours, depending on the duration of the rush hour [6]. When the traffic behaves as expected, the reversible lanes perform effectively. However, traffic in a big city is just a part of a complex system; numerous components and their interactions determine traffic flow patterns [7]. As a consequence, traffic congestion is a highly unpredictable phenomenon. It is hard to predict the traffic flow fluctuations at short time scales because it could be affected by an accident, a closed street, or faulty traffic lights kilometers away. We are now aware that reversible lanes cannot operate optimally because of these traffic flow fluctuations. We can even think of extreme scenarios where the traffic demand is the opposite of the expected one, resulting in a worse performance than the situation without any reversible lane. Since the beginning of 2020, many urban areas worldwide have experienced massive mobility changes due to the COVID-19 pandemic. The restrictions imposed by governments and the disease itself have shaped how we move in the city [8]. Alternative transport modes, such as bikes and scooters, have more demand than before the pandemic. In contrast, public transport systems, such as the metro and bus, are in much lower demand because people are trying to avoid crowded environments. Similarly, we can expect an increase in the number of people who prefer to use a particular vehicle due to the worry of becoming infected. Once the pandemic is over, there is a (...truncated)