Understanding taxi drivers＇ routing choices from spatial and social traces

Most of our learning comes from other people or from our own experience. For instance, when a taxi driver is seeking passengers on an unknown road in a large city, what should the driver do？ Alternatives include cruising around the road or waiting for a time period at the roadside in the hopes of finding a passenger or just leaving for another road enroute to a destination he knows （e.g., hotel taxi rank）？ This is an interesting problem that arises everyday in cities all over the world. There could be different answers to the question poised above, but one fundamental problem is how the driver learns about the likelihood of finding passengers on a road that is new to him （as he has not picked up or dropped off passengers there before）. Our observation from large scale taxi driver trace data is that a driver not only learns from his own experience but through interactions with other drivers. In this paper, we first formally define this problem as socialized information learning （SIL）, second we propose a framework including a series of models to study how a taxi driver gathers and learns information in an uncertain environment through the use of his social network. Finally, the large scale real life data and empirical experiments confirm that our models are much more effective, efficient and scalable that prior work on this problem.

Traffic Speed Interface Development in Route Choice Decision

In this paper,a method has been developed based on historic traffic data(speed),which helps the commuters to choose routes by their intelligence knowing the traffic conditions in Google maps.Data has been collected on basis of video analysis from several segments between Tuker Bazar and Bandar Bazar route.For each of the video footage,a reference length has been recorded with measurement tape for use in video analysis.A software has been also developed based on Java language to get the traffic information from historic data,which shows the output as images consisting of traffic speed details on the available routes by giving day and time limit as inputs.The developed models provide useful insights and helpful for the policy makers that can lead to the reduction of traffic congestion and increase the scope of intelligence of the road users,at least for the underdeveloped or developing country where navigation is still unavailable.

Combinatorial Model Involving Stochastic Choices of Destination, Mode and Route

Traffic assignment models are one of the basic tools for the analysis and design of transportation systems. However, the existing models have some defects. Considering the characteristics of Chinese urban mixed traffic and the randomness of transportation information, the author develops a combinatorial model involving stochastic choices of destination, mode and route. Its uniqueness and equivalance are also proved by the optimization theory.

Swarm Intelligence Algorithm Inspired by Route Choice Behavior

Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to in- corporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. Anew swarm intelligence algorithm inspired by travelers＇ route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization （PSO） algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm （RCBA）. The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.

Stochastic Route Choice Equilibrium Assignment for Travelers with Heterogeneous Regret Aversions

Most route choice models assume that people are completely rational.Recently,regret theory has attracted researchers'attentions because of its power to depict real travel behavior.This paper proposes a multiclass stochastic user equilibrium assignment model by using regret theory.All users are differentiated by their own regret aversion.The route travel disutility for users of each class is defined as a linear combination of the travel time and anticipated regret.The proposed model is formulated as a variational inequality problem and solved by using the self-regulated averaging method.The numerical results show that users'regret aversion indeed influences their route choice behavior and that users with high regret aversion are more inclined to change route choice when the traffic congestion degree varies.

Generating route choice sets with operation information on metro networks

In recent years, the metro system has advanced into an efficient transport system and become the mainstay of urban passenger transport in many mega-cities. Passenger flow is the foundation of making and coordinating operation plans for the metro system, and therefore, a variety of studies were conducted on transit assignment models. Nevertheless route choice sets of passengers also play a paramount role in flow estimation and demand prediction. This paper first discusses the main route constraints of which the train schedule is the most important, that distinguish rail networks from road networks. Then, a two-step approach to generate route choice set in a metro network is proposed. Particu- larly, the improved approach introduces a route filtenng with train operational information based on the conventional method. An initial numerical test shows that the proposed approach gives more reasonable route choice sets for scheduled metro networks, and, consequently, obtains more accurate results from passenger flow assignment. Recommendations for possible opportunities to apply this approach to metro operations are also provided, including its integration into a metro passenger flow assignment and simulation system in practice to help metro authorities provide more precise guidance information for passengers to travel.

Combinatorial Model Involving Travel Choice,Destination Choice and Route Choice

Considering random utility of transportation information, the author develops combinatorial model involving travel choice, destination choice and route choice. It's uniqueness and equivalance are also proved. Finally, an model's a1gorithm is given out.

Evaluating human behaviour during a disaster evacuation process:A literature review

The frequency of natural and man-made disasters has increased over the past few decades,which has doubled the significance of evacuation planning because it directly affects people’s lives and properties.How evacuees behave during a disaster and the methodologies to assess their behaviour are vital factors in managing any emergency scenario.For example,during the evacuation of a transportation network,users’amount of information and how they react are imperative to achieve a resilient response to a disaster.Even though there are numerous approaches for assessing evacuee behaviour,further research is needed to determine how and when each methodology should be employed.This paper intends to evaluate and classify the methodologies that have been presented up to date to create a more consistent approach to interpreting human behaviour during an evacuation process.The majority of studies focus on how people behave during an evacuation of a building,with less attention paid to how they behave during a transport evacuation.Among the proposed methodologies in this paper,the virtual reality approach,in individual behaviour,and agent-based models,in crowd behaviour,have more advantages than other approaches.Overall,a comparison between the proposed approaches is made in the discussion part.The output of this study provides the classifications and suggestions for researchers to pick an appropriate approach based on the types of problems,and some direction for future studies are introduced.To reach the research goal,177 papers have been reviewed between 1954 and 2022.

Does a large group of pedestrians follow the evacuation signs?An experimental study

Guidance signs are generally considered to be key in improving the efficiency of emergency building evacuations.During a group evacuation,how obedient is the group to the evacuation signs?Would different evacuation signs affect the judgment of evacuated groups?Evacuation experiments were conducted to explore the influence of evacuation signs on the path selection of evacuation groups.Fourteen classes with 514 participants participated in these experiments,and each class participated in one experiment.Six classes conducted the experiments without guidance signs,and the rest conducted the experiments with guidance signs.The influences of the sign’s color(red and green),pattern(arrow and running man),and status(whether it flashes)on the route selection were studied.The results show that(1)the evacuation signs significantly affect the route choices of the evacuated groups.(2)The herding effect during the evacuation process does not influence the guiding effect of evacuation signs.(3)Evacuation signs of different colors and patterns have different guiding effects,and the Green running man sign has the best effect.(4)The constant bright,or flashing evacuation sign corresponding to the flashing Red running man has a significant impact on the route selection of the evacuated group.The results may provide a reference for improving the emergency evacuation guidance model of office buildings or other buildings with a large flow of people and optimize ideas for the design and layout of emergency evacuation signs.

Analysing taxi customer-search behaviour using Copula-based joint model

The drivers of vacant taxis tend to cruise the road network searching new passengers,which leads to additional traffic congestion,air pollution and other problems.This study introduces a Copula-based joint model to analyse destination selection and route choice behaviour in the customer-search process.A multinomial logit model is used to analyse the destination selection behaviour,and a path size logit model is used to explore the routes choice behaviour.Accordingly,the joint model applied Copula function is then established to analyse the correlation between these two behaviours.The destination customer generation rate,destination distance,route customer generation rate,path travel time,cumulative intersection delay,path size and route length are selected as explanatory variables.The taxis trajectory data were collected by global positioning system in Xidan District of Beijing City from September 2014 to February 2015.Using the log-likelihood,Bayesian information criterion as evaluation indexes to measure the fitting result,the joint model applied Copula function has the highest goodness-of-fit.The effect of explanatory variables on customer search behaviour is discussed based on the parameter estimation results.The results of this study are helpful to understand the customer-search behaviour of taxi drivers to reduce operating costs and improve the efficiency of the taxi operation system.

Analyzing Behavior Differences of Occupied and Non-Occupied Taxi Drivers Using Floating Car Data

As the travel purpose of non-occupied taxies is to find new passengers rather than to arrive at the destination, large differences exist in the route choice behavior between the occupied and non-occupied taxies.With the assistance of geographic information system(GIS) and taxi-based floating car data(FCD), this paper investigates the behavior differences between occupied and non-occupied taxi drivers with the same origin and destination. Descriptive statistical indexes from the FCD in Shenzhen, China are explored to identify the route choice characteristics of occupied and non-occupied taxies. Then, a conditional logit model is proposed to model the quantitative relationship between drivers' route choice and the related significant variables. Attributes of the variables related to non-occupied taxies' observed routes are compared with the case of occupied ones. The results indicate that, compared with their counterparts, non-occupied taxi drivers generally pay more attention to choosing arterial roads and avoiding congested segments. Additionally, they are also found less sensitive to fewer traffic lights and shorter travel time. Findings from this research can assist to improve urban road network planning and traffic management.