Trip Distribution Research Articles

An activity space-based gravity model for intracity human mobility flows

Modeling and estimating urban human mobility flows is significant to multidisciplinary applications such as transportation engineering, urban planning and epidemic prevention. In recent decades, a wide range of trip distribution models for urban human mobility flows have been developed, among which the gravity model is the most classic and widely used. However, like most of the existing trip distribution models, the original gravity model adopts population as the proxy of urban region's attractiveness, which could not fully reflect the urban region's attractiveness as well as its temporal dynamics. Moreover, the original gravity model depends on the selection of proper distance decay functions, which require context-specific adjustable parameters derived from empirical mobility data. Taking advantage of the notion of activity space, we innovatively develop a proxy of urban region's attractiveness, and further propose a new activity space-based gravity (ASG) model. The case study results show that the ASG model significantly outperforms the original gravity model in estimating the overall human mobility flows and the hourly human mobility flows between urban regions. This study contributes to the state-of-the-art understanding of urban human mobility and human mobility flow modeling. The findings also provide insights into transportation management, infrastructure development and urban planning.

Exploring Empty Truck Behavior Using One-Year Probe Vehicle Data

Examining empty truck behavior can be beneficial in developing an efficient and environmentally friendly logistics system. This study used probe vehicle data from April 2019 to March 2020 in Kyushu, Japan, to explore empty truck behavior. The results revealed a difference in the empty truck ratio across the day of the week, time of the day, and month and the trip distribution of empty and loaded trucks. A nonhierarchical cluster analysis was used to classify vehicles based on empty/loaded situations. These results can be potentially used to reduce the number of empty trucks and improve the efficiency of logistics systems.

A risk minimization based approach for planning evacuation trip distribution

Most studies on evacuation strategies hitherto focus on routing and the resulting traffic pattern. While doing so, such studies generally consider evacuating people to the nearest shelter location (relief point). In this paper, we illustrate why a strategy of evacuating to the nearest relief point would not be the most risk-averse plan. We present a methodology for planning evacuation trip distribution to potential relief points in a risk-averse manner. The procedure explained would help policy makers to identify relief points where capacity must be improved while preparing for a disaster. We present a risk-minimizing formulation that prescribes evacuation trip distribution and corresponding capacity enhancement scheme (RETDC+) for the relief points. To assess the reduction in risk, we compare this formulation with two other formulations: one where risk-averse trip distribution is performed without enhancing the existing capacities (RETDC), and another where trips are distributed to the nearest relief points (PETDC). We envisage to employ failure probability values based on susceptibility maps that are often prepared for disaster response planning. The methodology is demonstrated for the city network of Gangtok, India which is prone to natural disasters such as landslides and flash floods. We illustrate that the evacuation trip matrix obtained by employing the proposed methodology is 20% more risk-averse for landslide and 27% for flash flood, respectively, than the evacuation trip matrix obtained by a naive distribution to the nearest relief points.

Freight trip distribution using spatiotemporal aggregate data: A modified collective flow diffusion model-based approach

The estimation of freight trip distribution is an important issue in freight transport studies, and collecting data on the spatiotemporal aggregated number of freight vehicles without trip information is relatively easy and inexpensive. In this paper, we propose a new model for freight distribution estimation, referred to as “TripCFDM.” Unlike the traditional collective flow diffusion model (CFDM), which solely relies on aggregate data, TripCFDM relies on aggregate data along with a small amount of trip data. CFDM estimates the probability of latent zonal freight trips based on aggregate data corresponding to the number of vehicles in each zone and time step. The use of the aggregate and small amounts of trip data ensures a realistic data acquisition environment. Our model is applied to freight trips in the Tokyo Metropolitan Area using the National Survey of Roads and Streets Traffic Conditions (Road Traffic Census). The results reveal that TripCFDM yields a significant improvement in the estimated probability compared with CFDM, particularly for intrazonal freight trips. However, the estimation results of interzonal trips obtained by CFDM and TripCFDM tend to be similar, suggesting that CFDM alone may be adequate to estimate the probability of interzonal trips with a sufficient degree of accuracy; nevertheless, TripCFDM is a better method for freight trip distribution estimation, including for intrazonal trips. The results of CFDM are not significantly affected by the time steps in the input data, whereas TripCFDM can yield higher accuracies with longer time steps. Regarding the number of zones and zone size, sufficient zone aggregation may be effective in improving the estimation accuracy because a detailed zonal division leads to a small probability of origin–destination trips with extensive computations. Based on this modified CFDM approach, origin and destination matrices for freight transport can be generated without large-scale surveys.

A flexible and scalable single-level framework for OD matrix inference using IoT data

This study proposes a flexible and scalable single-level framework for origin–destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources. The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources. We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia. We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in to vehicle navigation systems, and journey-to-work OD data collected by the Australian Census. We compare our OD inferences with those from the current version of the Strategic Adelaide Model (SAM), calibrated using data from traditional household travel surveys. We find remarkable consistency between our inferences and those from SAM, despite differences in input data and methodologies. For example, for the morning peak period, we predict the total number of trips made within GA to be equal to 556,000, while the corresponding prediction from SAM is equal to 484,000. The two predictions are within 15 per cent of each other. When we compare the spatial distribution of trips, in terms of origins and destinations, we find that our inferred OD matrix has an 86 per cent cosine similarity to the corresponding SAM matrix. Travel time predictions based on our OD inferences correspond closely to those predicted by the Google Directions API, lending further confidence in our inferences. In summary, our results show that the proposed framework can produce highly comparable ODM, trip production and trip attraction patterns to those inferred from traditional household travel survey-based transportation demand modelling methods.

Omega-3 enriched chick diet reduces the foraging areas of breeders in two closely related shearwaters from contrasting marine environments.

Seabirds have evolved several life-history characteristics to help buffer environmental stochasticity. However, particularly during the breeding season, seabirds may be affected by reductions in prey availability and localised oceanographic conditions caused by variations in the environment. The increase in sea surface temperature, triggered by accelerated global warming, is impairing phytoplankton production of omega-3 fatty acids (FAs). Here, we assessed the ecological role of omega-3 FAs on chick development and subsequently on breeder foraging behaviour in two closely related shearwater species foraging in contrasting marine environments. We supplemented chicks with omega-3 FA pills or with control placebo pills and monitored chick growth, chick health status and breeder at-sea foraging behaviour using global positioning system devices. We found that omega-3 chick supplementation reduced the 95% kernel utilization distribution of short trips of Cape Verde shearwaters, but overall, breeders kept a similar foraging pattern between treatments, potentially influenced by predictable prey patches off the West African coast. In contrast, for Cory's shearwaters, the parents of the omega-3 group greatly reduced the foraging effort. This suggests that the proximity to productive prey patches around the colony may help birds to adjust their effort and, therefore, energy expenditure, to changes in the development of their offspring, as driven by their nutritional status. Overall, our results suggest a link between a chick diet enriched in omega-3 FAs and parental foraging effort, providing insight into their ability to cope with a changing and increasingly stochastic marine environment.

Optimal Sampling Plan for Freight Demand Synthesis with Mode Choice: A Case study of Bangladesh

This paper uses a comprehensive experimental design to investigate the influence of various traffic count sampling plans for Bangladesh on the performance of the Freight Origin-Destination Synthesis model with Mode Choice (FODS-MC) developed by Kalahasthi et al. FODS-MC estimates a national-level freight demand model including trip distribution, mode choice, empty truck trips, and empty rail trips, where one of the key inputs is the freight truck and rail, traffic counts. The traffic count sample comprises three types of road links (national, regional, and zilla) and one category for the rail link across the country. A Box–Behnken Design (BBD) with a response surface for each of four FODS-MC parameters (distribution, mode choice, truck empty trips, and rail empty trips) is constructed. The results showed that the response surfaces are nonlinear planes for all parameters. There is no single optimal sampling plan for FODS-MC as each model parameter demands different distribution across the truck and rail links. The random and stratified samples perform almost similarly if less than 20% of the sample is collected. Minimizing the loss functions between the estimated and true parameters shows that a random sample between 20% and 25% of the truck and rail links estimates the best freight demand model. Overall, this research develops a framework to assist public practitioners in the optimum usage of the limited time and resources in collecting the traffic count data that could estimate the freight demand and mode choice models effectively.

Quality Assessment of OpenStreetMap’s Points of Interest with Large-Scale Real Data

OpenStreetMap (OSM) data are geographical data that are easy and open to access and therefore used for a large set of applications including travel demand modeling. However, often there is a limited awareness about the shortcomings of volunteered geographic information data, such as OSM. One important issue for the application in travel demand modeling is the completeness of OSM elements, particularly points of interest (POI), since it directly influences the predictions of trip distributions. This might cause unreliable model sensitivities and end up in wrong predictions leading to expensive misinterpretations of the effects of policy measures. Because of a lack of large-scale real-world data, a detailed assessment of the quality of POI from OSM has not been done yet. Therefore, in this work, we assess the quality of POI from OSM for use within travel demand models using surveyed real-world data from 49 areas in Germany. We perform a descriptive and a model-based analysis using spatial, demographic, and intrinsic indicators for two common trip purpose categories used in travel demand modeling. We show that the completeness of POI data in OSM depends on the category of POI. We further show that intrinsic indicators and indicators calculated based on data from other sources (e.g., land use or census data) are able to detect quality deficiencies of OSM data.

A realistic framework for volume-delay function determination

Volume-Delay Function (VDF) is a key input in several tasks including traffic assignment and trip distribution estimation. However, there is no clear framework to identify the VDF formula and parameters. Furthermore, the VDF performance in the oversaturated condition is uncontrolled. In this paper, most VDF-related aspects are investigated, including the key attributes, traffic model and VDF formulas, oversaturated condition assumptions, and optimization techniques. In each aspect, different methods are compared for best performance, while a new procedure is introduced, if necessary. The best traffic model and VDF formulas are nominated to satisfy a set of logic and statistical conditions, while a new optimization technique is presented. Furthermore, a new realistic assumption to control the VDF performance is discussed. This framework is applied on 32 worldwide roads of different categories and characteristics, which indicates accurate and realistic results. The results encourage for further investigation of the VDF performance in future work.

Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile.

The COVID-19 pandemic strongly affected the mobility of people. Several studies have quantified these changes, for example, measuring the effectiveness of quarantine measures and calculating the decrease in the use of public transport. Regarding the latter, however, a low level of understanding persists as to how the pandemic affected the distribution of trip purposes, hindering the design of policies aimed at increasing the demand for public transport in a post-pandemic era. To address this gap, in this article, we study how the purposes of trips made by public transport evolved during the COVID-19 pandemic in the city of Santiago, Chile. For this, we develop an XGBoost model using the latest available origin-destination survey as input. The calibrated model is applied to the information from smart payment cards during one week in 2018, 2020, and 2021. The results show that during the week of maximum restriction, that is, during 2020, the distribution of trips by purpose varied considerably, with the proportion of trips to work increasing, recreational trips decreasing, and trips for health purposes remaining unchanged. In sociodemographic terms, in the higher-income communes, the decrease in the proportion of trips for work purposes was much greater than that in the communes with lower income. Finally, with the gradual return to in-person activities in 2021, the distribution of trip purposes returned to values similar to those before the pandemic, although with a lower total amount, which suggests that unless relevant measures are taken, the low use of public transportation could be permanent.

Gender, class and daily mobility in a global city

The gendered distribution of trips linked to the domestic sphere and to care work have been highlighted by a range of feminist geographers and sociologists since the 1980s. Yet, little research has shed light on how class relations intersect with this gendered mobility. The study on intersection of class and gender is particularly relevant in Global cities as places that concentrate both highly-skilled and low-skilled female workers, connected to intertwined migration flows. This paper will use the Domestic Mobility Work as a conceptual framework to highlight how in a ‘small global city’ – Brussels – highly qualified female workers and mothers manage their gendered spatial constraints, mostly by calling on the services of lower class women. Through semi-structured interviews with high-profile female workers with children, it shows that delegating mobility workload is crucial for them to fulfill their career and to maintain an ideal of gender equality within the couple. But the interviewees also shed light on the challenge delegating care work poses to their identity as mothers. They also show the gendered relevance of mobility services offered by care and educational institutions, set up exclusively for the benefit of employees in the international sector. The article calls for further research on the intersections of class, gender and migration in global cities.

Policy impacts on the propensity to travel by HSR in the Amsterdam – London market

This paper investigates the impacts of possible measures to enhance HSR market share in the London-Amsterdam market. To this aim, we adopted a two-level aggregate nested logit model able to jointly model trip distribution and trip generation, thus taking into account that the tested policies may increase or decrease the overall demand. The model was applied to an extensive dataset about passenger flows traveling between London airports and HSR station and Amsterdam in the period 2015–2019. The results show that both the reduction of HSR fares and the application of an air ticket tax, albeit with different impacts in terms of stimulus or reduction effect on overall demand, are ineffective in increasing HSR market share, if not adequately supported by improving HSR service. Increasing HSR frequency and reducing HSR travel times constitute the best opportunities to increase HSR ridership by stimulating a higher substitution effect than modifications in relative fares. Lastly, the recent queueing at airports, following staff shortages and strikes, significantly lower air transport demand and potentially has a substantial upward effect on HSR market share.

Chaotic Characteristic Analysis of Dynamic Gravity Model with Fractal Structures via an Improved Conical Volume-Delay Function

Road traffic networks are chaotic and highly complex systems. In this paper, we introduce a dynamic gravity model that characterizes the behaviors of the O-D (origin-destination) traffic, such as equilibrium, period-doubling, chaos, and fractal in discrete time. In cases where the original cost function is used, the trip distribution model might degenerate into an all-or-nothing problem without the capacity constraints. To address this shortcoming, we propose substituting the original cost function with an improved conical volume-delay function. This new function retains some of the properties of the original cost function, and its parameters have the same meaning as those in the original function. Our analysis confirms that the double-constrained dynamic gravity model successfully characterizes complex traffic behavior because of the improved conical volume-delay function. Our analysis further shows that the three-parameter bifurcation diagram based on the period characteristics provides deep insight into the actual state of the road traffic networks. Investigating the properties of the model solutions, we further show that the new model is more effective in addressing the all-or-nothing problem.

Equilibria (equilibrium states) problem statement for city transport systems

Introduction. The article formulates the problems statement about equilibrium states in the model of a generalized transport system of the city, consisting of a street and road network, centers of mass gravity, places of residence of people, vehicles, as well as road users themselves, including passengers. The solution of these problems makes it possible to identify the equilibrium distributions of the system elements over various subsets of states (a subset of the elements of the road network; a subset of the center of mass gravity; a subset of trips of a certain type, etc.), depending on the type of vehicle, individual preferences, knowledge about the state of the transport system and other factors.At the same time, the transport system is considered as an object of research within the framework of the theory of macrosystems. A set of problems statements on the search for equilibrium states of the transport system for various modelling objects has been compiled for various structural levels (scales) of the objects under consideration.Materials and methods. In this paper, the theory of transport macrosystems is applied, which follows from a wellknown scientific discipline - the theory of macrosystems. Among its tasks there are statements about the distribution of elements over subsets of states and problems about the equilibrium of the system as a whole. In macroscopic systems, by definition, the stochastic behavior of a large number of elements transforms the deterministic behavior of the system as a whole. A macro system is thus a dynamic converter of the chaotic behavior of elements into a certain set of behavior parameters (phase variables) forming a space of small dimension. Therefore, within the framework of the theory of macrosystems, the basic concepts of entropy maximization at equilibrium states of the system are used. In this case, the distribution function of macrostates is selected depending on the method of filling some states with elements from the corresponding subsets; the necessary values of a priori probabilities and proofs of parametric properties of models of macrosystems with various statistics (Fermi-, Einstein- and Boltzmann-distributions). On the basis of the theory of macrosystems, for example, problems are solved to find equilibrium in such systems as: 1) megapolis with its functional and spatial structures (probabilistic states of hierarchical systems), 2) transport networks of cities formed by the movement of vehicles and residents of the city between different areas (distribution of trips along routes in the network); 3) logistics systems in the interregional exchange of products (problems of economic equilibrium in the exchange of resources).Results. The paper presents the results of research concerning the uniform description of the elements of the road network and the centers of mass gravity as components of the general transport system of the city (agglomeration) in the framework of the theory of transport macrosystems. At the same time, the study identifies various structural levels of description that can be used to solve particular problems, for example, finding equilibrium in individual subsets of the transport system, such as groups of centers of mass gravity of a certain type, or traffic flows on routes, stretches, network sections, etc.Discussion and conclusions. Within the framework of the work, the following tasks were solved: a description of the structural levels of the objects of the road network and the centers of mass gravity as the main components of the model of transport systems was developed; the formulation of problems about the equilibrium states of transport systems at the corresponding structural levels was developed; the analysis of the obtained methodology was performed; a methodological analogy is established between different subsets of states at the same structural level, for example, between the centers of mass gravity and elements of the road network as objects of modeling by methods of the theory of macrosystems (this analogy can be extended to other subsets of states in transport systems, for example, types of transport systems, travel purposes, parking spaces, subsystems of the intelligent transport system and much more).

COVID-19 in city and municipal area: socio-economic analysis in Khulna, Bangladesh

PurposeIn Bangladesh (a middle-income and densely populated country) where socio-economic factors act badly on human activities during COVID-19. This research mainly focused on observing the socio-economic aspects of the Pandemic on human life between city and municipal areas.Design/methodology/approachThis research relied on Khulna City Corporation (KCC) and Paikgacha Municipality of Bangladesh. A random sampling technique was adopted for choosing 622 stakeholders (318 in the city and 304 in the municipal area). Here, the socio-economic factors have been fixed based on the literature review and expert opinion. This study explored two mainstream social and economic issues affected by the Pandemic. Several statistical tests were performed to find the relationship among Socio-economic factors.FindingsThe study shows that the Pandemic caused great harm to city areas rather than municipal areas. The city is faced with tremendous pressure on the economic aspect as well. Besides, the pandemic affects savings, education sectors, food habits and other factors in both areas. The trip distribution also differs between the study areas and the mobility pattern shows that people migrated to rural areas from city space during the Pandemic.Originality/valueThis research will assist in focusing on a micro-level perspective in the future to analyze socio-economic changes. Moreover, it can help to point out the administrative prospects in the future.

Electric Fences for Dockless Bike-Sharing Systems: An Electric Fence-Planning Framework for a Dockless Bike-Sharing System Based on a Land Parcel Subdivision and Regional Coverage Maximization

Dockless bike sharing (DBS) is convenient for bike users because it can solve their last-mile problems. However, dockless bikes that are disorderly parked in public spaces can disturb normal traffic. In planning electric fences for DBS systems, the maximum coverage location problem (MCLP) identifies locations suitable for electric fences. However, the MCLP model ignores the regional boundaries of bike use and causes substantial errors in coverage assessments. In this article, we present a novel framework for planning electric fences, accounting for both trip distribution and area shapes. We first introduce a dynamic land parcel subdivision algorithm, which can reasonably divide the city into fine-scale regions concerning the distributions of spatial parking demands. Then, we formulate the electric fence-planning problem as a regional coverage maximization problem. We develop an accelerated maximum coverage model with complementary coverage (MCMCC) to locate electric fence areas, which can improve solution accuracy and reliability. The DBS data of Shenzhen, China, are used to validate the framework. The study findings indicate that MCMCC produces more robust and accurate results than the MCLP model because the planning solutions are not affected by changes in the shapes and sizes of the regions. The framework can also automatically and accurately determine electric fence capacities and locations.

HIGH SCHOOL ZONING POLICY IMPACT ON TRANSPORTATION IN BANDUNG CITY WEST JAWA PROVINCE

The enactment of the zoning policy through the Minister of Education and Culture Regulation (Number 14/2018) concerning the Admission of New Students states that the admission priority would be given to those who live within the radius of the closest zone from the school. From a transportation perspective, this policy has a potential positive impact in creating a more balanced school trip distribution pattern, and would further reduce the total trip length and improve transportation performance. Taking Bandung City as a case, this study aims to evaluate the impact of the Penerimaan Peserta Didik Baru/PPDB (New Student Admission) based on zoning system policy on the travel distribution patterns of high school students in this city. The method used in this research is spatial distribution analysis and qualitative descriptive analysis. The research performs a before-after analysis of comparison to analyze the impact of the policy on schooling trip distribution. The results show that the zoning policy stimulates the total change of the average schooling trip length of high school students in Bandung by 35%. This research also concludes that the schooling trip distribution pattern is no longer centered in the city center where favorite schools were located.
 Keywords: Government Policy, School Zoning, Bandung City Transportation.

Structural change and spatial pattern of intentional travel groups: A case study of metro riders in Hong Kong

Amid the COVID-19 pandemic, face-to-face contacts decreased but still existed despite people's fear of virus infection and governments' social gathering restrictions. These interactions influenced virus transmission routes, if any and reflected people's essential social interactive demands in the city. In this article, we identified people who intentionally travel as groups (ITGs) to characterize social interactions before and amid COVID-19. To systematically understand ITGs' mobility patterns, an ITG structure was defined and measured in multiple dimensions, including composition, function, size, intensity, quality, and spatiotemporal distribution. Based on a longitudinal smartcard dataset in Hong Kong spanning the year of 2020, we operationalized the ITG structure in the local metro system and examined whether and to what degree the structure changed during the pandemic. We found that ITGs' activities fluctuated as the pandemic progressed and their changes differed across different ITG groups. The long-distance ITGs saw the most significant change. The spatial distribution of persistent ITG trips before and amid the pandemic became spatiotemporally more concentrated. Stations with similar ITG indices clustered in proximity, and features of station areas like residents' education level and quantity of commercial facilities could well predict stations' ITG indices. In other words, inequal distribution of essential facilities and opportunities could notably influence ITGs, social contacts, and socioeconomic benefits brought about by them amid COVID-19. The findings provide insights concerning both resilience management amid the crisis and the long-term planning of essential facilities and services that facilitate group-based outgoings and activities.

Analyzing time-varying trip distributions with a random-effect spatial OD dependence model.

This paper proposes a random-effect spatial OD (origin-destination) dependence model to investigate varying trip distributions over time. By proposing a maximum likelihood estimation with spectral decomposition methods, the effects of spatial dependences and the unobservable zonal heterogeneity at the origin and destination can be estimated simultaneously. A series of numerical experiments and a real-world trip distribution study with cellular signaling data collected in Hangzhou, China, are conducted. This paper enriches the existing literature by developing (1) an innovative specification to allow for random effects in existing spatial OD dependence models; (2) an innovative estimation method to obtain the values of parameters and improve model fittings; and (3) a set of numerical experiments and an empirical trip distribution analysis that jointly captures spatial effects (spatial interaction and spatial OD dependences), and the unobservable zonal heterogeneity. This paper can equip policymakers with an effective tool for analyzing the OD travel flow over time which is a groundwork for making appropriate transportation policies.

Influence of the Spatial Distribution of Jobs in Intervening Opportunities Models

This article analyzes the influence of spatial distribution of jobs, which represents intervening opportunities, in aggregated and disaggregated trip distribution models that generated eight origin–destination matrices. The observed trip matrix was obtained from the Urban Transport Master Plan (PDTU) of the Rio de Janeiro Metropolitan Region in Brazil. The estimated matrices were calculated using the gravity, intervening opportunities and Naïve Bayes models, organized at two aggregation levels. These models’ performance was evaluated considering the aggregation level, the model approach adopted (aggregated or disaggregated), and the spatial distribution of the intervening opportunities. The results indicated that the intervening opportunities exert a positive influence on all models, under any level of zone size (subdistricts or neighborhoods). In the aggregate models, the variable “jobs” insertion improved the models’ performance when applied at a higher aggregation level (subdistricts). The best-case scenario showed a coefficient of determination (R2) equal to 0.8705, obtained in a disaggregated model with intervening opportunities applied in subdistricts.