Trip Distribution Research Articles

Optimal Deployment of Cordon Sanitaire with Available Testing Capacity

During the COVID-19 pandemic, authorities in many places have implemented various countermeasures, including setting up a cordon sanitaire to restrict population movement. This paper proposes a bi-level programming model to deploy a limited number of parallel checkpoints at each entry link around the cordon sanitaire to achieve a minimum total waiting time for all travelers. At the lower level, it is a transportation network equilibrium with queuing for a fixed travel demand and given road network. The feedback process between trip distribution and trip assignment results in the predicted waiting time and traffic flow for each entry link. For the lower-level model, the method of successive averages is used to achieve a network equilibrium with queuing for any given allocation decision from the upper level, and the reduced gradient algorithm is used for traffic assignment with queuing. At the upper level, it is a queuing network optimization model. The objective is the minimization of the system’s total waiting time, which can be derived from the predicted traffic flow and queuing delay time at each entry link from the lower-level model. Since it is a nonlinear integer programming problem that is hard to solve, a genetic algorithm with elite strategy is designed. An experimental study using the Nguyen-Dupuis road network shows that the proposed methods effectively find a good heuristic optimal solution. Together with the findings from two additional sensitivity tests, the proposed methods are beneficial for policymakers to determine the optimal deployment of cordon sanitaire given limited resources.

Development of Trip Generation and Trip Distribution Models for Car-sharing Considering Land Use and Transport Characteristics

Development of Trip Generation and Trip Distribution Models for Car-sharing Considering Land Use and Transport Characteristics

Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so far. Therefore, this paper proposes a forecasting model focused on the trip distribution of tourists who travel with the suburban tourist railway. Based on the analysis of the characteristics of tourists’ trips and the use of the trip chain method, the frequency, order, distance, and visiting volume of stay points of the trips of tourists have been intensively studied. Then, a tourist trip distribution forecasting model was built in this paper. It uses the Entropy-Maximizing theory to predict trip chain distribution probability and obtain the distribution of tourists within the city. A case study that takes the H city as an example was conducted to test the proposed model. The results of this case show that the output of the model can reflect the real trip distribution characteristics of tourists very well, which demonstrates the applicability and effectiveness of the proposed model.

Discovering source areas of disease outbreaks based on ring-shaped hotspot detection in road network space

ABSTRACT The accurate discovery of disease-outbreak source areas plays a critical role in the effective containment of epidemics at an early stage. Existing relevant methods are mostly implemented by tracing the source of disease outbreaks directly from the distribution of confirmed cases in the Euclidean space. In reality, in most respiratory infectious diseases, crowd gathering caused by resident trips significantly increases the risk of exposure to potentially infected persons, making it the driving force behind the outbreak of the disease. In light of this, this study proposes a network-constrained ring-shaped hotspot detection method based on the classical spatial scan statistic model. This new method can be used to determine the sizes of the scanning window in an adaptive manner by using the information of the trip distance distributions. Considering the centered traffic analysis zone in each hotspot as a candidate outbreak source area, a multi-factor coupling model was designed by characterizing both the areal vibrancy and resident trip distributions to further identify the potential disease-outbreak source areas. A case study on the outbreaks of COVID-19 in Wenzhou, China, was carried out to evaluate the practicability and effectiveness of the proposed method. The comparison results demonstrate the effectiveness of the proposed method for detecting areas and sources of outbreaks.

Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips

Unbalanced distribution of trips during the time is one of the factors influencing traffic congestion at some hours of a day. Identifying the significant factors on travelers’ departure time choice and predicting their behavior helps maintain the balance in the time distribution of trips. For this purpose, this study employs and compares two machine learning and probabilistic approaches to model the departure time choice, including four choices, morning peak, noon peak, evening peak, and non-peak hours. Probabilistic support vector machine (PSVM) and multinomial logit (MNL) models calibrated based on the origin–destination data of Qazvin, and the evaluation and comparison of these two models made based on two applications of identifying the significant factors on the departure time and predicting the departure time. In terms of interpretability, the MNL model results have an indisputable advantage due to the lack of interpretable coefficients and parameters in the PSVM model. On the other hand, machine learning models’ predictive power partially covers the disadvantage of not being interpretable. The results show that the PSVM model can predict the departure time with 53.96% accuracy than the 49.98% accuracy of the MNL model. The maximum balanced accuracy for predicting morning peak, noon peak, and non-peak options is 69%, 53%, and 60%, respectively; obtained by the PSVM model and the MNL model predicts the evening peak option with a balanced accuracy of 52% more accurate than PSVM.

Transportation Planning Using Conventional Four Stage Modeling : An Attempt for Identification of Problems in a Transportation System

In the formative development of urban transportation planning, travel forecasting is essential. Various travel simulation techniques are used to estimate possible traffic scenarios. It's been used to analyze road capability, transportation system developments, and new land use strategies and policies. The solution to travel decisions is generally obtained if the models are done in sequence. Many attempts are made to simulate the variety of choices made by travellers as a part of a response to a particular highway system, related transit system, and connected policies. For this purpose number of assumptions are considered regarding the decisions made by the people, factors considered by them, and their reaction as a response to the alternatives available in the transportation system. Generally, the process of travel simulation follows trip generating at the trip generation zone and moves through different links of the network and nodes. It ultimately ends at the trip attraction zone. The simulation method is known as Traditional Four Steps Transportation Modeling, and it includes the four basic models namely Trip Generation, Trip Distribution, Mode Choice, and Traffic Assignments. This paper it is tried to focus on the process of this traditional four-step transportation modeling system. The modeling method calculates a simplified transportation network from the city of Ulhasnagar, India.

On Time-Dependent Trip Distance Distribution with For-Hire Vehicle Trips in Chicago

For transportation system analysis in a new space dimension with respect to individual trips’ remaining distances, vehicle trips demand has two main components: the departure time and the trip distance. In particular, the trip distance distribution (TDD) is a direct input to the bathtub model in the new space dimension, and is a very important variable to consider in many applications, such as the development of distance-based congestion pricing strategies or mileage tax. For a good understanding of the demand pattern, both the distribution of trip initiation and trip distance should be calibrated from real data. In this paper, it is assumed that the demand pattern can be described by the joint distribution of trip distance and departure time. In other words, TDD is assumed to be time-dependent, and a calibration and validation methodology of the joint probability is proposed, based on log-likelihood maximization and the Kolmogorov–Smirnov test. The calibration method is applied to empirical for-hire vehicle trips in Chicago, and it is concluded that TDD varies more within a day than across weekdays. The hypothesis that TDD follows a negative exponential, log-normal, or Gamma distribution is rejected. However, the best fit is systematically observed for the time-dependent log-normal probability density function. In the future, other trip distributions should be considered and also non-parametric probability density estimation should be explored for a better understanding of the demand pattern.

Spatial and Temporal Characteristics of Urban Tourism Travel by Taxi—A Case Study of Shenzhen

Tourism networks are an important research part of tourism geography. Despite the significance of transportation in shaping tourism networks, current studies have mainly focused on the “daily behavior” of urban travel at the expense of tourism travel, which has been regarded as an “exceptional behavior”. To fill this gap, this study proposes a framework for exploring the spatial and temporal characteristics of urban tourism travel by taxi. We chose Shenzhen, a densely populated mega-city in China with abundant tourism resources, as a case study. First, we extracted tourist trips from taxi trajectories and used kernel density estimation to analyze the spatial aggregation characteristics of tourist trip origins. Second, we investigated the spatial dependence of tourist trips using local spatial autocorrelation analysis (Getis-Ord Gi*). Third, we explored the correlations between the tourist trip origins and urban geographic contextual factors (e.g., catering services and transportation facilities) using a geographically weighted regression model. The results show the following: (1) the trends between the coverage of tourist travel networks and the volume of tourist trips are similar; (2) the spatial interaction intensity of urban tourism has grouping and hierarchical characteristics; and (3) the spatial distribution of tourist trips by taxi is uneven and influenced by the distribution of urban morphology, tourism resources, and the preferences of taxi pick-up passengers. Our proposed framework and revealed spatial and temporal patterns have implications for urban tourism traffic planning, tourism product development, and tourist flow control in tourist attractions.

Smart Region Mobility Framework

A smart city describes an urban setting which aims to effectively apply ICT technologies to help improve the well-being of its citizens and reduce the negative impacts of urbanization. The priority areas considered in the Global Smart City Index (SCI) by the Institute for Management Development’s (IMD) World Competitiveness Centre were key infrastructures and technologies in (1) health and safety, (2) mobility, (3) activities (e.g., recreational spaces), (4) opportunities (work and school), and (5) governance. A smart region is a term used to extend the concept of a smart city into both urban and rural settings to promote a sustainable planning approach at the regional level. A direction that must be considered is the adoption of a “Smart Region Mobility Framework” to effectively transform our urban and rural regional transportation networks. This research study focused on the development of the smart region mobility framework for an island region group in the Philippines. The smart region goal is to integrate intelligent transportation system (ITS) platforms such as advanced public transportation system (APTS), advanced traveler information system (ATIS), and advanced rural transportation system (ARTS) to the local public transportation route plans (LPTRP) of the region. The activities include the data collection, analysis, and evaluation of multimodal regional transportation networks and social services infrastructure. The transportation network modeling process follows the four-step transportation planning process of trip generation, trip distribution, modal-split analysis, and trip assignment. Based on the analysis of 6 provinces, 16 cities, and 114 municipalities included in the study, there are two cities identified as smart city candidates. One of the smart city candidates is designated as the smart city regional center. In the context of a smart region, the available social services (e.g., employment opportunities, education, and health services) in the designated smart cities can also be made accessible to connected cities/municipalities through ease of transportation and mobility services in the region. Lastly, the study presented the implementation of data flow architecture of the smart region mobility framework, and the regional traveler information system using mobile and web application services.

The Post-Pandemic Recovery of Transport Activity: Emerging Mobility Patterns and Repercussions on Future Evolution

The expectations for post-COVID recovery of transport activity point towards a gradual return to normality, once the pandemic is under control and mobility restrictions end. The shock to society and economy has, however, caused a number of behavioural changes that can influence the evolution of the transport sector. We analyse the main factors that can influence future supply and demand and explore how they may affect trip generation, distribution and modal split in passenger transport. We combine several conventional and innovative data sources with a detailed strategic transport model at the EU level, in order to present quantitative estimates under various scenarios. New remote work patterns or personal risk avoidance attitudes can lead to increased levels of car ownership and use. Public policy priorities in the aftermath of the pandemic would need to address the emerging challenges and adopt measures that can sustain the shift to active travel, support public transport, railways and aviation and stimulate innovation in transport technologies and services.

Effect of Transportation Parameters on Traffic Accident in Urban Areas Comparison study of ANFIS With Statistical Analysis

Traffic accidents present a serious problem for both developed and developing countries and have become an urgent matter to tackle in all large metropolitan areas. This study aims to perform a deep comprehensive analysis of the traffic accidents issue in Istanbul, one of the world’s most populous cities. The accidents were classified and its intensities were presented on Istanbul map using a GIS tool. Furthermore, the performance of Negative Binomial Regression analysis and Adaptive Neuro-Fuzzy Inference System (ANFIS) model was assessed. Data collection of independent variables included distribution of trips, percentage of street parking, rate of car ownership, street density and population density. Trips were divided into three categories, passenger car, minibus and bus trips. The results showed that four legs intersection got the highest proportion of accidents among the other types with (40%). It also demonstrated that increasing both the percentage of bus trips and the percentage of street parking will decrease the traffic accident rate. Furthermore, the implementation of ANFIS model increased the accuracy of forecasts and reduced errors more than the regression model.

Modelling Trip Distribution Using the Gravity Model and Fratar's Method

Trip Distribution is a difficult and significant model in the urban transportation planning process. This paper creates and assesses a satisfactory model of the trip distribution stage for the Nasiriyah city by using two models, Gravity and Fratar methods. A large sample was used for developing the model. The research methodology depends on discussing the theoretical fundamentals of the various methods for estimating the trips distribution and examining the suitability of these fundamentals for the conditions of the selected study area. Two different models had been used, namely; Frater and Gravity model. These models were calibrated using real data. The study tests the accuracy of the models, including overall statistical assessments of the predicted movements. Finally, the study recommended to use Fratar Method. These results had been confirmed to the literature that, if the area is a homogenous growth, the best model is the growth factor (Fratar's method) and if the area is experiencing rapid changes. The gravity model will produce satisfactory results because it takes into account the competition in different land uses.

Estimating Pedestrian Flows on Street Networks

Problem, research strategy, and findings City governments and planners alike commonly seek to increase pedestrian activity on city streets as part of broader sustainability, community building, and economic development strategies. Though walkability has received ample attention in planning literature, most planners still lack practical methods for predicting how development proposals could affect pedestrian activity on specific streets or public spaces at different times of the day. Cities typically require traffic impact assessments (TIAs) but not pedestrian impact assessments. In this study I present a methodology for estimating pedestrian trip generation and distribution between detailed origins and destinations in both existing and proposed built environments. Using the betweenness index from network analysis, I introduce a number of methodological improvements that allow the index to model pedestrian trips with parameters and constraints to account for pedestrian behavior in different settings. I demonstrate its application in the Kendall Square area of Cambridge (MA), where estimated foot traffic is compared during lunch and evening peak periods with observed pedestrian counts. Takeaway for practice The proposed approach can be particularly useful for TIAs, neighborhood plans, and large-scale development projects, where pedestrian flow estimates can be used to guide pedestrian infrastructure and safety improvements and public space investments or for locating pedestrian priority streets during the COVID-19 pandemic.

Modelling Stochastic Electricity Demand of Electric Vehicles Based on Traffic Surveys—The Case of Austria

Battery-powered electric mobility is currently the most promising technology for the decarbonisation of the transport sector, alongside hydrogen-powered vehicles, provided that the electricity used comes 100% from renewable energy sources. To estimate its electricity demand both nationwide and in individual smaller communities, a calculation based assessment on driving profiles that are as realistic as possible is required. The developed model based analysis presented in this paper for the creation of driving and thus electricity load profiles makes it possible to build different compositions of driving profiles. The focus of this paper lies in the analysis of motorised private transport, which makes it possible to assess future charging and load control potentials in a subsequent analysis. We outline the differences in demand and driving profiles for weekdays as well as for Saturdays, Sundays and holidays in general. Furthermore, the modelling considers the length distribution of the individual trips per trip purpose and different start times. The developed method allows to create individual driving and electric vehicle (EV) demand profiles as well as averaged driving profiles, which can then be scaled up and analysed for an entire country.

Demand forecasting model analysis for Al-kufa road network

For the design of transport facilities and services, as well as for planning, expenditure, and policy development, travel demand modelling studies are important. The goal of this research is to improve and apply projected models for each stage of the Al-Kufa city travel demand forecast process that includes, in addition to the road network database, social, economic, location and land-use features. The study region is separated into four sectors to accomplish the study goal. For data collection, full interview, and home questionnaire, two methods have been used. In September 2019, the data collection began. The six-purpose trip generation models (home base work, educational, religious, social, shopping, other trips) were advanced by stepwise regression method (Multiple Linear Regression, MLR) using software SPSS version 20. The results indicated that the determination coefficient R2 for (Y) (the total number of trips is 0.88 and considered to be a very good correlation. Travel time and cost of each utility mode are the key parameters that have affected the mode option models and the gravity model used in Al-Kufa city for the trip distribution method. The assignment models for the allocation of traffic in the Al-Kufa road network for the base year of the analysis are used by the (Stochastic user equilibrium), the network suffers from high congestion with near and higher v/c links 1.

Detecting anomalous spatial interaction patterns by maximizing urban population carrying capacity

Rapid urbanization in China has prompted plenty of urban facilities to be constructed with the expectation of harmonizing with the rapid growth of urban population. However, regarding the spatial interactions produced by cross-area human mobility, the diversity and variability of residents' trip requirements inevitably cause the deviations of the real interaction patterns from the optimal status determined by the current allocation of urban facilities. To maximize the utility of urban facility allocation, we designed a bipartite network-based approach to explore anomalous spatial interaction patterns within cities. First, considering the potential area attractiveness, a weighted origin-destination bipartite network was constructed to structure the spatial interactions between traffic analysis zones. Then, a branch and bound (BnB) based augmenting path algorithm was proposed to optimize the distribution of spatial interactions, which can maximize the urban population carrying capabilities. Finally, anomalous interaction patterns causing both overload and underload were detected through comparisons between the actual and optimal spatial interaction distribution. The experimental results show that the two types of anomalous interaction patterns have significantly different spatial distribution characteristics. Through further analyzing the relationships between the two types of anomalous interaction patterns and urban evolution process, this study can also provide targeted decision supports for the accommodating of urban facility allocations to the distributions of resident trips in space.

Planned Special Event Travel Demand Model Development

When a planned special event (PSE) is mentioned, large and international organizations are considered. These organizations attract so many people all around the world to local points. However, relatively small scale PSEs such as ordinary league games that are organized once every two weeks that impact the daily traffic of the cities especially metropolitans, are neglected. This paper focuses on the travel demand modelling of the ordinary super league games in Istanbul. As a purpose of this paper, in order to obtain a customizable and standalone PSE model, survey design and data collection procedures, a new methodology for trip generation, trip distribution, and modal split steps of the traditional 4-step demand modelling are considered. With the opportunity provided by the newly proposed methodology, unlike most previous studies in literature, all trips and activities in the same day with the PSE are taken into the modelling process. Because of the nature of the PSEs, participants prefer to perform additional (derived) activities in the time between leaving the origin and joining the PSE. Accordingly, 1, 2, and 3-step groups are defined, and the shape of trip length distributions are different not only from the peak hours but also from each other. At the end, the model estimation and development of the PSE travel demand model are presented with conclusions and suggestions.

A causal inference approach to measure the vulnerability of urban metro systems

Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inference approach for estimating station-level vulnerability in metro systems. The empirical analysis is based on large-scale data on historical incidents and population-level passenger demand. This analysis thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop four empirical vulnerability metrics based on the causal impact of disruptions on travel demand, average travel speed and passenger flow distribution. Specifically, the proposed metrics based on the irregularity in passenger flow distribution extends the scope of vulnerability measurement to the entire trip distribution, instead of just analysing the disruption impact on the entry or exit demand (that is, moments of the trip distribution). The unbiased estimates of disruption impact are obtained by adopting a propensity score matching method, which adjusts for the confounding biases caused by non-random occurrence of disruptions. An application of the proposed framework to the London Underground indicates that the vulnerability of a metro station depends on the location, topology, and other characteristics. We find that, in 2013, central London stations are more vulnerable in terms of travel demand loss. However, the loss of average travel speed and irregularity in relative passenger flows reveal that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes.

Development of a Complex Network-Based Integrated Multilayer Urban Growth and Optimisation Model for an Efficient Urban Traffic Network

Previous research studies of traffic networks are mainly based on planar networks and less considered the influence of multilayer networks, which illustrate and represent different appropriate urban traffic modes. Development of rail and road networks is inseparable from the development of a prosperous urban area; thus, research on multilayer networks has scientific potential and fulfils a real need. In this paper, a framework of complex network based integrated multilayer urban growth and optimisation model (CNIMUGOM) is proposed, to analyse the complex relationships between the traffic network structure, the population growth, and the urban land-use. The innovation of this paper is the combination of the traffic complex multilayer networks and the “Four Step Model” (which stands for trip generation, trip distribution, model split, and traffic assignment steps). With the multiobjective, multilayer network coevolution and optimisation model, a more efficient traffic network layout was generated based on different land-use, population density, and travel speed scenarios. Then, this paper has proved that the proposed CNIMUGOM can save the traffic network construction investment, reduce the travel cost, make the urban traffic network more efficient, and decrease the total traffic flow amount. This research has connected the recent complex multilayer network related study and traditional urban economic model based study. The findings of the study afford to improve the current land-use and traffic integrated models and can provide traffic network planning suggestions for urban agglomeration development.

Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data

A reliable estimation of public bicycle trip characteristics, especially trip distribution and duration, can help decision-makers plan for the relevant transport infrastructures and assist operators in addressing issues related to bicycle imbalance. Past research studies have attempted to understand the relationship between public bicycle trip generation, trip attraction and factors such as built environment, weather, population density, etc. However, these studies typically did not include trip distribution, duration, and detailed information on the built environment. This paper aims to estimate public bicycle daily trip characteristics, i.e., trip generation, trip attraction, trip distribution, and duration using points of interest and smart card data from Nanjing, China. Negative binomial regression models were developed to examine the effect of built environment on public bicycle usage. Totally fifteen types of points of interest (POIs) data are investigated and factors such as residence, employment, entertainment, and metro station are found to be statistically significant. The results showed that 300 m buffer POIs of residence, employment, entertainment, restaurant, bus stop, metro station, amenity, and school have significantly positive effects on public bicycle generation and attraction, while, counterintuitively, 300 m buffer POIs of shopping, parks, attractions, sports, and hospital have significantly negative effects. Specifically, an increase of 1% in the trip distance leads to a 2.36% decrease in the origin-destination (OD) trips or a 0.54% increase of the trip duration. We also found that a 1% increase in the number of other nearby stations can help reduce 0.19% of the OD trips. The results from this paper can offer useful insights to operators in better estimating public bicycle usage and providing reliable services that can improve ridership.