In this paper, a multi-valued cellular automata model for mixed non-motorized traffic flow is proposed and the simulation results are studied. The mixed. The authors present an analysis of multiple-valued linear cellular automata (CA) and their properties over GF(q). An application for pseudorandom pattern g. This letter develops an improved multi-value cellular automata model for heterogeneous bicycle traffic flow taking the higher maximum speed of electric bicycles.
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Abstract Simulation, as a powerful tool for evaluating transportation systems, has been multivalued cellular automata used in transportation planning, management, and operations. Most of the simulation models are focused on motorized vehicles, and the modeling of nonmotorized vehicles is ignored.
The cellular automata CA model is a very important simulation approach and is widely used for motorized vehicle traffic.
This paper improves on these two CA models and also compares their multivalued cellular automata. In the research for this paper, many cases, featuring different values for the slowing down probability, lane-changing probability, and proportion of EBs, were simulated, while the fundamental diagrams and capacities of the proposed models were analyzed and compared between the two models.
Field data were collected for the evaluation of the two models. The results show that the M-CA model exhibits more stable performance than the two-lane NS model and provides results that are closer to real bicycle traffic.
Introduction Traffic flow theories are generally divided into two branches: The macroscopic traffic flow models are based on fluid dynamics and are mostly used to elucidate the relationships between density, volume, and speed also called the fundamental diagram in various traffic conditions.
The microscopic traffic models, on multivalued cellular automata other hand, describe the interaction between individual vehicles.
The microscopic traffic models generally include car-following models and cellular automata CA models. The car-following model is the most important model, describing the detailed movements multivalued cellular automata vehicles proceeding close together in a single lane.
Multi-value cellular automata model for mixed non-motorized traffic flow
There multivalued cellular automata been many car-following models produced in the literature over the past 60 years, such as stimulus-response models, safety distance models, action point models, fuzzy-logic-based models, and optimal velocity models [ 2 — 5 ].
For a broader review, multivalued cellular automata to Brackstone and McDonald [ 6 ] and Chowdhury et al. Recently, CA models have emerged as an efficient tool for simulating highway traffic flow because of their easy concept, simple rule, and speed in conducting numerical investigations.
The rule model, proposed by Wolfram [ 8 ], was the first CA model to be widely used for traffic flow.
Discrete Dynamics in Nature and Society
The NS model and the many improved versions of it reproduce some basic and complicated phenomena such as stop and go, metastable states, capacity drop phenomena which means the capacity of road experiences a large drop under critical density conditionsand synchronized flow in real traffic conditions.
Most of the aforementioned microscopic traffic models have been developed only for motorized vehicles. Few of them have been used for modeling non-motorized vehicles such as bicycles, tricycles, electric bicycles, and motorcycles because of the complicated characteristics of such vehicles movements.
With the increasing usage of bicycles, some researchers have begun focusing on modeling the operation of bicycle facilities. Their simulation results showed that, once the randomization effect is considered, the multiple states multivalued cellular automata deterministic M-CA models disappear multivalued cellular automata unique flow-density relations exist.
They found the transition from free flow to congested flow to be smooth in one model but of second order in the other. Lan and Chang [ 11 ] developed inhomogeneous CA models to elucidate the interacting movements of cars and multivalued cellular automata in mixed traffic contexts.
The CA models were validated by a set of field-observed data and the relationships between flow, cell occupancy a proxy of densityand speeds under different traffic mixtures and road lane widths were elaborated.