Inferring the structure of pedestrian flows at a transportation hub.

Dynamic Route Choice Modelling of the Effects of Travel Information using RP Data

In this paper, a network-wide anticipatory control (AC) framework, incorporating drivers' route choice behaviour, is proposed. The proposed AC, consisting of two main levels of control and route choice, explicitly accounts for road users' responses to the implemented control. Perimeter control, modelled in an MPC framework for real-time applications, optimizes inter-regional transferring flow at the network level. User equilibrium is established through a logit route choice model and solved using the method of successive averages (MSA). The modelling approach adopted is based on macroscopic fundamental diagram (MFD), which provides a unimodal low-scatter relationship between density and outflow of any region within a network. Numerical results indicate that the proposed AC outperforms no control and basic control cases and shows promising results in alleviating congestion and deriving the network to near-system optimum traffic condition, under various demand patterns and levels.

A new modeling method that describes bicycle route or destination choice behavior is presented. Although there are numerous bicycle users in Japan, the urban transportation planning process often treats bicycles and pedestrians as a single mode. Therefore, a methodology by which to evaluate and analyze bicycle demand needs to be developed. A bicycle route choice model that describes the relationship between route choice behavior and facility characteristics (e.g., road width or sidewalk) has been proposed. This model can be applied to the planning of bicycle road networks. The data from a bicycle trip survey conducted in Japan are used to study the characteristics of the model. The model is applied to study access railway station choice (destination choice). The model can produce a better fit than can a conventional model.

Studying drivers' route choice behavior under the influence of travel information is important because it provides insight to improve the effect of travel information on traffic environment. This paper mainly aims to study the impact of travel information on travelers' route choice behavior at different departure time. Multinomial logit model (MNL) is used to model travelers' route choice behavior under travel information at different departure time. By comparative study on drivers' route choice under travel information at different departure time, it is found that (1) the content of travel information significantly affect travelers' route choice, (2) travelers' departure time significantly influences travelers' route choice response to travel information, (3) travelers' socio-demographics have considerable correlation with travelers' route choice behavior.

This paper proposes an intelligence-based approach to predict passengers' route choice behavior, which is crucial to the effective utilization of transportation stations and affects passenger comfort and safety. The actual route choice decisions of passengers are extremely difficult to mimic as they involve human behavior. A comprehensive methodology for capturing route choice behavior is still lacking because extensive labor and time resources are required to collect passenger movement data from different stations. In this paper, a four-month site survey was carried out to collect actual route choice behavior information in nine transportation stations in Hong Kong during peak hours. We developed an intelligent model to capture passengers' route choice decision-making that achieved prediction accuracy of 86%. The applicability of this intelligent route choice model is demonstrated by optimizing the number of gates in a transportation station to inform the spatial design of the station.

Crowds are a feature of large cities, occurring not only at mass gatherings but also at routine events such as the journey to work. To address extreme crowding, various computer models for crowd movement have been developed in the past decade, and we review these and show how they can be used to identify health and safety issues. State-of-the-art models that simulate the spread of epidemics operate on a population level, but the collection of fine-scale data might enable the development of models for epidemics that operate on a microscopic scale, similar to models for crowd movement. We provide an example of such simulations, showing how an individual-based crowd model can mirror aggregate susceptible-infected-recovered models that have been the main models for epidemics so far.

When only one type of point is distributed in a region, clustered points can be seen as an anomaly. When two different types of points coexist in a region, they overlap at different places with various densities. In such cases, the meaning of a cluster of one type of point may be altered if points of the other type show different densities within the same cluster. If we consider the origins and destinations (OD) of taxicab trips, the clustering of both in the morning may indicate a transportation hub, whereas clustered origins and sparse destinations (a hot spot where taxis are in short supply) could suggest a densely populated residential area. This cannot be identified by previous clustering methods, so it is worthwhile studying a clustering method for two types of points. The concept of two-component clustering is first defined in this paper as a group containing two types of points, at least one of which exhibits clustering. We then propose a density-based method for identifying two-component clusters. The method is divided into four steps. The first estimates the clustering scale of the point data. The second transforms the point data into the 2D density domain, where the x and y axes represent the local density of each type of point around each point, respectively. The third determines the thresholds for extracting the clusters, and the fourth generates two-component clusters using a density-connectivity mechanism. The method is applied to taxicab trip data in Beijing. Three types of two-component clusters are identified: high-density origins and destinations, high-density origins and low-density destinations, and low-density origins and high-density destinations. The clustering results are verified by the spatial relationship between the cluster locations and their land-use types over different periods of the day.

The article discusses the main aspects of the architectural and planning organization of service facilities for transport hubs. The main blocks of the functional planning structure of transport interchange hubs (T.I.HUB), elements of their structure and principles of placement in the structure of large cities have been formed in the center, in the structure of residential areas, in historical city centers, and in the contact zone of urban and suburban development. The work substantiates the relevance of the development regarding the design of transport hubs (T.HUB)s in the structure of modern cities, which are constantly and systematically developing, increasing the need for the transportation of an increasing number of passengers. The dynamic development of modern infrastructural cities, especially large ones, require a revision of the norms and dimensions of transfer stations, the landing front of stops, the reorganization of the inner spaces of the transport hubs (T.HUB), etc. The design and construction of transport hubs can be carried out in a completely new construction, reconstruction or reconstruction with modernization, or in the difficult conditions. Already today, many big cities are suffering from traffic jams bordering on collapse, especially during peak hours. Therefore, this problem must be solved in all possible ways, especially attention should be paid to the architectural and planning organization of service facilities, which are located at the transport hub (T.HUB). A transport interchange hub is a nodal element of the city's planning structure, which allows organizational transfer of passengers between various types of urban and external (intercity, international) passenger transport or between different lines of the same type of transport in an urban structure. For example, transport hubs (T.HUB) are possible in the structure of the functioning of a railway transport hub from one railway line to another. Transport interchange hubs (T.I.HUB)can be specialized or multifunctional, and include a number of facilities for passing passenger services and social infrastructure: accumulative lobbies in front of the entrance group ("entrance" - "exit"), a foyer with digital validation of electronic tickets and cards on different routes of passengers; cargo and goods rooms for baggage claim (at airports, river stations and railway stations); customs premises (if we are talking about international flights departing from transport hubs (T.HUB); intercepting parking lots and taxi and bus stands; public service facilities; shops, catering establishments; financial institutions, etc. As noted earlier, we can talk about six types of transport hubs (T.HUB) in the structure of the designed and existing nodes on land, underground, river, rail, sea and air transport. Thus, modern transport hubs can be organized not only in the city structure, but also in ports, railways. railway stations and airports, which can significantly expand their typological range of service facilities.

Advanced demand data collection technologies for multi modal strategic modelling

Along with the increasing proportion of urban public transportation trip, pedestrian flow in transportation hub areas increased. For effectively improving the emergency handling ability of related management apartments and preventing the incident of pedestrian congestion, this paper studied the method of pedestrian flow forecast in Beijing transportation hub areas. Firstly, 34 typical sidewalks in Beijing transportation hub areas were surveyed to obtain 2200 valid data. Secondly, correlation analysis was used to analyze the relationship between pedestrian flow and its influential factors. 11 significant influential factors were extracted. Thirdly, forecasting model was established with modular neural network. The surveyed pedestrian flow sample was fuzzy clustered according to the regional land use where the transportation hub existed. Then, membership function based on the distance measure was constructed. Through fuzzy discrimination, online selection for the subnetwork of the information can be achieved. Consequently, the self-adaptation of the neural network on information processing was improved. Finally, this paper tested the pedestrian flow sample of a transportation hub in Beijing. It was concluded that the accuracy of pedestrian flow forecasting model using modular neural network was higher than other neural network models. There was also improvement in the adaptability to environment.

Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit

Traffic congestion has become a part of commuters’ life in Jakarta for several years. Even though 3-in-1 traffic regulation has been implemented since 1992 in order to reduce the number of car driver travelling in busy corridors during morning and evening peak hours, and Bus Rapid Transit (BRT) has been operated since 2004, which is aimed to attract car drivers to use public transport, the traffic congestion problem in Jakarta has not been solved. The condition is even predicted to worse if the transport facilities in Jakarta are not improved. In order to find effective measures for reducing car use and improving public transport attractiveness in Jakarta, better understanding of commute mode and route choice behavior would be advantageous. This paper reports the first results of data analysis regarding dynamic behavior of commuters’ mode and route choice in Jakarta. The data were collected using GPS devices including questionnaire sheets during a one-week period. 93 commuters participated in the survey. Even though commute trips are routine trips and therefore often assumed to be static, the results show the presence of dynamic behavior in choosing both modes and routes for commuting. The dynamic behavior is as a way to avoid traffic congested roads and 3-in-1 corridors, and to maintain trip-chaining activities/stops. Car drivers and motorcyclists change frequently their routes, especially during work-to-home trips. Motorcyclists were more dynamic in choosing their routes than car drivers. A unique pattern of mode and route choice behavior was found which can be used for developing mode and route choice model in Jakarta.

Traffic congestion has become a part of commuters’ life in Jakarta for several years. Even though 3-in-1 traffic regulation has been implemented since 1992 in order to reduce the number of car driver travelling in busy corridors during morning and evening peak hours, and Bus Rapid Transit (BRT) has been operated since 2004, which is aimed to attract car drivers to use public transport, the traffic congestion problem in Jakarta has not been solved. The condition is even predicted to worse if the transport facilities in Jakarta are not improved. In order to find effective measures for reducing car use and improving public transport attractiveness in Jakarta, better understanding of commute mode and route choice behavior would be advantageous. This paper reports the first results of data analysis regarding dynamic behavior of commuters’ mode and route choice in Jakarta. The data were collected using GPS devices including questionnaire sheets during a one-week period. 93 commuters participated in the survey. Even though commute trips are routine trips and therefore often assumed to be static, the results show the presence of dynamic behavior in choosing both modes and routes for commuting. The dynamic behavior is as a way to avoid traffic congested roads and 3-in-1 corridors, and to maintain trip-chaining activities/stops. Car drivers and motorcyclists change frequently their routes, especially during work-to-home trips. Motorcyclists were more dynamic in choosing their routes than car drivers. A unique pattern of mode and route choice behavior was found which can be used for developing mode and route choice model in Jakarta.

To improve the effects of travel information on the traffic environment, it is of great importance to understand drivers’ route choice behavior under the influence of travel information. This paper applies stated preference survey techniques to collect data, and based on these data, a model of drivers’ route choice under the impact of travel information is developed by a multinomial logit model (MNL). The objective of the model is to determine the potential interplay among real-time travel time information, socio-demographics of drivers and route choice. From the estimated models and the analysis of the model, it can be discovered that (1) real-time travel information--travel time, travel time variation and the extent of road congestion-- has significant effects on drivers’ route choice behavior. (2) Drivers pay more attention to travel time information than travel time variation. (3) Among socio-demographics information of drivers, drivers’ age, driving experience and gender have considerable correlations with drivers’ route choice behavior.

To study the effect of en-route information on driver’s route choice behavior, a dynamic route choice modeling approach is proposed, which takes the driver’s knowledge updating process into consideration. A Bayesian network is developed to describe the en-route travel time updating process. Within the framework of the cumulative prospect theory, a choice model is conducted to analyze the driver’s route choice behavior at each decision node. A numerical example is carried out to illustrate the application of the dynamic route choice modeling approach. The result demonstrates that the route choice behavior considering en-route information is a dynamic process. The traditional route choice model based on cumulative prospect theory without considering en-route information is also employed as a reference in the experiment. From the comparison, the dynamic route choice modeling approach in which a driver’s knowledge of making en-route decisions is taken into account has a better goodness of fit. A stated preference survey is carried out to investigate drivers’ route choice behaviors under different traffic scenario. The result indicates that the proposed approach could provide a more accurate description to driver’s route choice behavior under the conditions of uncertainty.