Modeling electric bike–car mixed flow via social force model

Abstract

Because of the convenience and the quickness, electric bikes gradually become the main travel mode of urban resident in China, and electric bike-car mixed flow becomes the new characteristics of urban traffic flow. But their role in mixed traffic flow and their effects on cars have become important problems for transportation designers. Mixed traffic flow simulation model provides the designers an effective tool to acquire the relation between flow and density in a new design scheme, achieve solutions for optimal design features before implementation, and help to make the design more rational. Based on the electric bike-car mixed flow characteristics and social force model, this article proposes a microscopic model which can represent the behavior of electric bike-car mixed flow in road segment. First, the inner and outer forces determining the movement mechanism of electric bikes and cars were analyzed, respectively; second, we developed a modified social force model to describe the electric bikes and cars behaviors; third, the social force model was modified and extended for mixed traffic flow by considering the interactions between electric bikes and cars, lanechanging behavior, and conflict avoidance behavior. The interaction parameters of the social force-based model for electric bike-car mixed flow are estimated using empirical data. The established microscopic model can be used to estimate critical flow of separating vehicle and electric bike and determine proper electric bike lane width according to different electric bike requirements by simulating.

Social Force Model-Based Adaptive Parameters Collision Avoidance Method Considering Motion Uncertainty of the Pedestrian

“…Their main function are to maintain safer driving for both drivers and pedestrians [1]- [3], especially on non-signalized road. In some countries in Southeast Asia, electric bikes are more and more involved in the interaction of traffic flow due to their convenience and quickness [4]. Users of this type of transportation blur the line between vehicles and pedestrians, as all traffic participants can move freely in the same space.…”

“…For most research mentioned above, the motion of the obstacle was known in advance, such as being fixed in one position or moving along a predetermined trajectory by a given speed profile. Although other research, such as [4] and [28], adopted SFM for more realistic obstacle movement, there was also no randomness in the proposed SFM, which means the motion of obstacles can be predicted accurately in such researches. But, when considering the individual difference among pedestrians and the nuances of different shared space (like ages of pedestrians or visibility of area), the precise motion prediction of pedestrians is difficult to achieve, which also can be found in the results of research on modelings for similar mixed traffic scenarios, such as [17].…”

Exploring Microscopic Characteristics of Bicycle Riders’ following Behaviors in a Single-File Movement

“…Andresen et al [9] calibrated a car-following model called the Necessary Deceleration Model (NDM) for bicycle traffic and explained that the NDM can realistically model the free acceleration of a cyclist and the behavior of cyclists moving in a group. Qu et al [10] developed a microscopic model based on the social force model by Helbing and Molnar [11] to simulate the mixed traffic flows of electric bikes and cars. The force components of this model first considered the movement mechanisms and behaviors of electric bikes and cars separately.…”

Towards Building a Naturalistic Cycling Dataset Capturing Bicycle/Car Interactions

“…The observed literature gap between vehicular and bicycle traffic research is mostly justified by the scarcity, and even the non-existence, of naturalistic cycling data. Most of the existing research that investigated bicycles as a means of transportation [1][2][3][4][5][6] were in relation to investigating the interactions of bicycles with cars and other possible entities. Technically speaking, a significant portion of those studies falls under, either the Cellular Automata (CA) model that involves discretizing the time and space domain using a non-continuous cell grid such as the work of [1,2,4]; or the social force model approach [5,6] because of its advantages in terms of simulating dynamic lateral dispersion characteristics of mixed traffic.…”

“…Most of the existing research that investigated bicycles as a means of transportation [1][2][3][4][5][6] were in relation to investigating the interactions of bicycles with cars and other possible entities. Technically speaking, a significant portion of those studies falls under, either the Cellular Automata (CA) model that involves discretizing the time and space domain using a non-continuous cell grid such as the work of [1,2,4]; or the social force model approach [5,6] because of its advantages in terms of simulating dynamic lateral dispersion characteristics of mixed traffic. However, while these models offered a concise theoretical framework for the simulation of bicycle longitudinal and lateral traffic behavior in a mixed traffic environment, they were limited in their validation work due to the lack of naturalistic data capturing such interactions.…”