Destination Zone Research Articles

Migration and Land-Use and Land-Cover Change in Burkina Faso: a comparative case study

<p>In the Sahelian country of Burkina Faso, West Africa, population pressure, poor resource management, and reduced rainfall have exacerbated land degradation. A rapidly growing population coupled with high rates of internal rural migration and thirty years of desiccation have resulted in profound land-use/land-cover change (LULCC) throughout the country. In the Central Plateau and northern regions of Burkina Faso, land degradation has historically stimulated large-scale out-migration toward more fertile areas in the south. While some northern provinces are being rehabilitated by Soil and Water Conservation (SWC) projects, southern provinces, considered more "pristine", have been neglected. In recent decades, researchers have attributed the initiation of land degradation processes in southern regions to this influx of migrants from the north. This study presents an empirical controlled case study between two provinces to better understand the dynamics of migration and LULC. One province, Bam Province in the north, has long been a zone of departure while Sissili Province in the south has long been a destination zone. Using a regional political ecology framework, we integrate a time series of LULCC data with demographic census data and local narratives to compare migration and LULCC trends in Bam and Sissili from 1975 to 2013. We find that in-migration correlates with substantial and dramatic LULCC while out-migration is associated with only moderate LULCC. This controlled comparison also suggests that local land-use/land-cover change and migration dynamically interact. As environmental conditions in Bam improve and Sissili deteriorate, long-term trends of either out- or in-migration for either province stabilize, and can even become reversed.</p><p><strong>Key Words</strong>: Burkina Faso, LULCC, migration, regional political ecology</p>

Evaluation of a technology platform utilizing Bluetooth low energy (BLE) beacons to measure movement of animals, assets and personnel throughout a pig production network

Evaluation of a technology platform utilizing Bluetooth low energy (BLE) beacons to measure movement of animals, assets and personnel throughout a pig production network

Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data

There has been a recent push towards using opportunistic sensing data collected from sources like automatic vehicle location (AVL) systems, mobile phone networks, and global positioning system (GPS) tracking to construct origin-destination (O-D) matrices, which are an effective alternative to expensive and time-consuming traditional travel surveys. These data have numerous drawbacks: they may have inadequate detail about the journey, may lack spatial and temporal granularity, or may be limited due to privacy regulations. Taxi trajectory data is an opportunistic sensing data type that can be effectively used for O-D matrix construction because it addresses the issues that plague other data sources. This paper presents a new approach for using taxi trajectory data to construct a taxi O-D matrix that is dynamic in both space and time. The model’s origin and destination zone sizes and locations are not fixed, allowing the dimensions to vary from one matrix to another. Comparisons between these spatiotemporal-varying O-D matrices cannot be made using a traditional method like matrix subtraction. Therefore, this paper introduces a new measure of similarity. Our proposed approaches are applied to the taxi trajectory data collected from Lisbon, Portugal as a case study. The results reveal the periods in which taxi travel demand is the highest and lowest, as well as the periods in which the highest and lowest regular taxi travel demand patterns take shape. This information about taxi travel demand patterns is essential for informed taxi service operations management.

An Origin-Destination Matrix Estimate for Baghdad City Based on GIS

Baghdad suffers a deficiency in the application of urban transportation planning process, especially in selecting the suitable transport policies to solve transportation problems. One of the important inputs to the transportation planning process is the Origin – Destination Matrix. The O-D matrix is the travel demand between the pair of origin and destination zones and is one of the necessary goals of transportation studies. Estimation of an O-D matrix using the conventional process requires the collection of a huge amount of data. In Baghdad city, there has been no O-D matrix formulated. Accordingly, a prior O-D matrix is estimated in this study. The present research methodology is based on the estimated O-D matrix for Baghdad city in 1987 (prior matrix) and then updated to 2014 using the collected traffic count data as the basis for travel forecasting. The results of this study provide a guide to the local transportation agencies to select the right transport policies, maximize their revenue and better allocate their resources.

An Innovative Method for Estimating the Spatial Distribution of Parking Demand in Different Areas

Parking facilities in the urban road network are of the most essential parts of the transportation and traffic system. Considering that many users are not interested in parking in or near the destination zone, or in some cases it is not basically possible to park in this area, the parking demand in each area is not merely associated with the actual land use of that area and is sometimes affected by the land use of the surrounding area. Therefore, estimating the spatial distribution of parking demand is essential to accurately determine the real parking demand, especially in non-marginal urban areas. In conventional methods of estimating the parking demand in each area, it is assumed that the parking areas are located in the same area or distributed uniformly in the surrounding areas, without considering the desirability of the surrounding areas, which has sometimes led to unrealistic estimations. Accordingly, new methods, capable of estimating the real parking demand in each area, are needed. In present study, an innovative method is proposed to predict the spatial distribution of parking demand and then it is applied to the central area of Tehran.

Modelling the attraction of travel to shopping destinations in large-scale modelling

Development of major shopping centres continues even though online shopping is increasing. This has implications for mode and destination choice for shopping travel and therefore also for sustainability, which need to be considered in planning policy. In this paper, we estimate models for shopping travel using an unusually rich data set of shopping attractions. We find that shopping travel is best represented in three separate models: consumables in short and long activity segments and durables. In all of these models, we show that representing nearby attractions outside the destination zone adds to the measured attraction. For long activity consumables and for durables, the addition of secondary attractions within 2 km of the main destination gives the best models. For short activity consumables, both 2 km and 5 km add to the model, but 5 km is slightly better. Furthermore, we find significant within-zone correlation in the consumables models but are unable to find significant between-zone correlation, indicating that zone boundaries have some behavioural meaning for shopping travellers, but larger areas are not viewed in this way. Shopping attractions with a specifically Swedish impact, Systembolaget (official alcohol outlet in Sweden) and IKEA, proved to be important in all the models. These attractors work better as part of the size than as part of the utility, indicating that they appear to be separate attractors of trips, rather than as adding to the utility of other attractors. The models are also applied in two policy scenario analyses in which the impacts of new IKEA establishments and availability of Systembolaget in all zones on destination and mode choice are assessed.

Freight Demand Forecasting Considering Economic Growth Factors

This work proposes a methodology to project future freight demand for all commodity types that begin and end in each geographical region and the amount of freight that moves between all origin and destination pairs. Following the traditional four-step demand forecasting framework, the procedure corresponds to trip generation and trip distribution analysis for interregional freight demand. Using future economic growth factors from macroeconomic and input-output models, the amounts of freight production and attraction in each analysis zone are forecasted and taken as given. Subsequently, an iterative matrix balancing method is applied to determine the estimated freight shipment demand for all origin and destination zone pairs. The proposed algorithm is applied to generate predicted future freight demand within the United States from 2010-2050 in five-year increments based on the national freight demand data from 2007. Four different scenarios are proposed that consider variations in both global economic growth and environmental regulation. This study will assist transportation planners and decision makers in public and private sectors to assess how future freight delivery demand on the national scale considering various future global economic growth and environmental policy scenarios will affect various issues such as air quality and human health problems.

A dynamic taxi traffic assignment model: A two-level continuum transportation system approach

This paper proposes a two-level continuum transportation system approach to modeling a dynamic taxi traffic assignment (DTTA) problem in a dense network with real-time traffic information provision and three types of vehicles, including private cars, occupied taxis, and vacant taxis. The proposed approach treats the dense network as a continuum in the first level, in which private car and occupied taxi drivers are free to choose their paths in a two-dimensional continuous space. The proposed approach also divides the modeling region into many identical squares to form a cell-based network in the second level, in which the cells are classified into two categories: target cell with an acceptable expected rate of return (EROR) to vacant taxi drivers and non-target cell with an unacceptable EROR. The EROR associated with a cell is the ratio of the cumulative expected profit of a taxi driver who successfully picks up a customer during the customer search that starts from that cell to the sum of expected search time for this customer and expected occupied travel time to serve this customer. Based on the cell-based network, we develop a cell-based intervening opportunity model to capture the fact that vacant taxi drivers can meet a customer on the way to their destination zones and estimate the EROR. Each vacant taxi driver has a mixed strategy to determine his/her customer-search direction according to the EROR: Each vacant taxi driver in a target cell selects its neighbor cells with maximum EROR, and each vacant taxi driver in a non-target cell selects the travel time-based shortest path to his/her target cell. Meanwhile, each private car driver chooses the path that minimizes his/her own generalized travel cost, and each occupied taxi driver chooses the path that minimizes his/her customer's generalized in-vehicle travel cost. In our model, traffic density in the system is governed by the conservation law (CL), and the flow directions of different vehicles are determined by the path-choice strategies of their drivers, which are captured by Hamilton–Jacobi (HJ) equations. Both the proposed CL and HJ equations can be solved by the Lax–Friedrichs scheme, which forms the backbone of the developed solution algorithm. Finally, numerical examples and a case study are used to demonstrate the properties of the model, the performance of the solution algorithm, and the value of using our methodology for estimating network performance.

Mamminasata BRT User Trip Characteristics for the Design of Demand Modelling Method for a New BRT Line

Indonesia started to developed largely the MRT line, especially the BRT. A new BRT Corridor implementation need a Passenger Demand Prediction. Thus, a Special BRT Passenger Demand Modelling Method for a New BRT Line need to be developed. This attempt needs a sufficient knowledges on the Existing BRT User Trip Characteristics. Mamminasata BRT User Trip Characteristics Survey were executed on Corridor 2 and Corridor 3, during morning peak hour. The result indicates that the BRTs are used mainly for schooling and working trips, the BRT passengers are the previous public transport and motorcycle users. The trip origin zone is extended 3 km to the left and to the right of the BRT corridor, while the trip destination zone is extended 1.5 km to the left and to the right of the BRT corridor. The embarking connecting trip modes are dominated by motorcycle, public transport and becak, while the alighting connecting trip modes are dominated by walking tri

Estimating Path Travel Costs for Heterogeneous Users on Large-Scale Networks: Heuristic Approach to Integrated Activity-Based Model–Dynamic Traffic Assignment Models

Integrating activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) have gained attention from transportation planning agencies seeking tools to address the arising planning challenges as well as transportation policies such as road pricing. Optimal paths with least generalized cost are needed to route travelers at the DTA level, while at the ABM level, only the least generalized cost information is needed (without fully specified paths). Thus, rerunning (executing) the least generalized cost path-finding algorithm at each iteration of ABM and DTA does not seem to be efficient, especially for large-scale networks. Furthermore, storing the dynamic travel cost skims for multiclass users as an alternative approach is not efficient either in regard to memory requirements. In this study, the aim was to estimate the least generalized cost so as to be used in destination and mode choice models at the ABM level. A heuristic approach was developed to use the simulated vehicle trajectories that were assigned to the optimal paths in the DTA level to estimate different cost measures, including distance, time, and monetary cost associated with the least generalized cost path for any given combination of the origin, destination, and departure time (ODT) and value of time. The proposed approximation method presented in this study used vehicle trajectories, aligned with the origin–destination direction and located in a specific boundary shaping an ellipse around the origin and destination zones at a certain time window, to estimate travel costs for the given ODT and user class. Numerical results for two real-world networks suggest the applicability of the method in large-scale networks in addition to its lower computational burden, including solution time and memory requirements, relative to other alternative approaches.

Methodology for a dump design optimization in large-scale open pit mines

Modern large-scale open pit mines move hundreds of thousands of tonnes of material daily, from the loading sources to the destination zones, whether these are massive mine dumps or, to a lesser ext...

통행사슬 구조를 이용한 교통카드 이용자의 대중교통 통행종점 추정

본 연구는 선행연구에서 제시하고 있는 통행기점 정보만을 제공하고 있는 불완전한 대중교통카드 자료로부터 대중교통 통행의 종점을 통행사슬 구조를 이용하여 추정할 수 있는 모형의 국내 자료 적용 가능성을 살펴보고 모형 적용 결과를 제시하였다. 이를 위해 본 연구는 부산에서 2014년 10월 주중에 수집된 선불 교통카드 승 하차 태그 원시자료 1,846,252건을 대상으로 하루 동안 한 대중교통 이용자가 발생시킨 일련의 통행들을 시 공간적으로 연계시켜 통행사슬을 형성하고, 대중교통 이용자의 결측 종점을 연속된 다음 통행의 승차지점 또는 최초 승차지점이 속한 교통존으로 추정하였다. 모형 검증을 위해 대중교통 통행종점이 관측된 자료에 모형을 적용한 결과 실제 통행종점과 추정 통행종점의 일치도는 82.4%로 나타났으며 이 때 통행종점으로 추정된 정류장과 실제 하차 정류장간 거리의 오차는 최소가 되는 것으로 나타나 제안모형의 유용성은 높은 것으로 분석되었다. 통행사슬 구조를 이용한 통행종점 추정 모형을 종점결측 통행에 적용했을 때 종점결측 통행의 비율은 적용 전 71.40%(718,915통행)에서 21.74%(218,907통행)로 감소하였으며 종점추정이 불가한 218,907통행의 대부분은 모형 적용이 불가한 일일 통행횟수 '1회'인 통행(169,359통행, 77.37%)인 것으로 나타났고, 일일 통행횟수가 '2회 이상'인 통행의 종점결측 비율은 69.56%에서 모형 적용 후 6.27%로 크게 감소하였다. 한편 통행종점 추정 모형 적용에 따른 존간 통행 및 존내 통행분포의 변화를 비교하기 위해 순위상관계수 및 카이제곱 적합도 검정을 수행하였으며, 분석 결과 통행종점 추정 모형 적용에 따라 각 중존별 통행량의 순위는 변화하지 않으나 통행량 분포는 유의한 변화를 보였다. 따라서 통행사슬 구조를 이용한 교통카드 이용자의 통행종점 추정 모형 적용은 통행종점이 결측된 불완전 대중교통카드 자료가 수집되고 있는 도시의 대중교통 통행패턴을 보다 현실적으로 반영할 수 있게 도움을 줄 것으로 판단된다. Some previous researches suggested a transit trip destination inference method by constructing trip chains with incomplete(missing destination) smart card dataset obtained on the entry fare control systems. To explore the feasibility of the transit trip destination inference method, the transit trip chains are constructed from the pre-paid smart card tagging data collected in Busan on October 2014 weekdays by tracing the card IDs, tagging times(boarding, alighting, transfer), and the trip linking distances between two consecutive transit trips in a daily sequences. Assuming that most trips in the transit trip chains are linked successively, the individual transit trip destination zones are inferred as the consecutive linking trip's origin zones. Applying the model to the complete trips with observed OD reveals that about 82% of the inferred trip destinations are the same as those of the observed trip destinations and the inference error defined as the difference in distance between the inferred and observed alighting stops is minimized when the trip linking distance is less than or equal to 0.5km. When applying the model to the incomplete trips with missing destinations, the overall destination missing rate decreases from 71.40% to 21.74% and approximately 77% of the destination missing trips are the single transit trips for which the destinations can not be inferable. In addition, the model remarkably reduces the destination missing rate of the multiple incomplete transit trips from 69.56% to 6.27%. Spearman's rank correlation and Chi-squared goodness-of-fit tests showed that the ranks for transit trips of each zone are not significantly affected by the inferred trips, but the transit trip distributions only using small complete trips are significantly different from those using complete and inferred trips. Therefore, it is concluded that the model should be applicable to derive a realistic transit trip patterns in cities with the incomplete smart card data.

Development of destination choice models for pedestrian travel

Most research on walking behavior has focused on mode choice or walk trip frequency. In contrast, this study is one of the first to analyze and model the destination choice behaviors of pedestrians within an entire region. Using about 4500 walk trips from a 2011 household travel survey in the Portland, Oregon, region, we estimated multinomial logit pedestrian destination choice models for six trip purposes. Independent variables included terms for impedance (walk trip distance), size (employment by type, households), supportive pedestrian environments (parks, a pedestrian index of the environment variable called PIE), barriers to walking (terrain, industrial-type employment), and traveler characteristics. Unique to this study was the use of small-scale destination zone alternatives. Distance was a significant deterrent to pedestrian destination choice, and people in carless or childless households were less sensitive to distance for some purposes. Employment (especially retail) was a strong attractor: doubling the number of jobs nearly doubled the odds of choosing a destination for home-based shopping walk trips. More attractive pedestrian environments were also positively associated with pedestrian destination choice after controlling for other factors. These results shed light on determinants of pedestrian destination choice behaviors, and sensitivities in the models highlight potential policy-levers to increase walking activity. In addition, the destination choice models can be applied in practice within existing regional travel demand models or as pedestrian planning tools to evaluate land use and transportation policy and investment scenarios.

Evaluating the role of land use and transport policies in reducing the transport energy dependence of a city

In this paper a land use and transport model is presented to calculate an indicator of “Transport Energy Dependence” (TED) in order to support the delivery of sustainable urban and transport plans. The model is based on a mathematical description of the transport system, where transport mode choice follows ideal simple rules based on distance from origin to destination and transit network accessibility. For each transport mode unit energy consumption, capacity and load factor are considered. Flows of trips are optimally assigned between origin and destination zones in such a way that transport energy is minimised. Energy ideally required for home-to-school/university travel is assessed as these journeys contribute a significant number of daily trips within a city. In particular, the model was applied to the urban area of Catania, a medium-sized town in Italy, for different scenarios, including improvements in the transit system and in pedestrian/cycling accessibility. The methodology proved to be suitable to evaluate the potential impact of land use and transport policies in terms of transport energy dependence, separating it from behavioural considerations.

Travel Mode Detection Exploiting Cellular Network Data

There has been growing interest in exploiting cellular network data for transportation planning purposes in recent years. In this paper, we utilize these data for determining mode of travel in the city of Shiraz, Iran. Cellular data records -including location updates in 5minute time intervals- of 300,000 users from the city of Shiraz has been collected for 40 hours in three consecutive days in a cooperation with the major telecommunications service provider of the country. Depending on the density of mobile BTS’s in different zones of the city, the user location can be located within an average of 200 meters. Considering data filtering and smoothing, data preparation and converting them to comprehensible traces is a large portion of the work. A novel approach to identify stay locations is proposed and implemented in this paper. Origin-Destination matrices are then created based on trips detected, which shows acceptable consistency with current O-D matrices. Finally, Travel times for all trips of a user is estimated as the main attribute for clustering. Trips between same origin and destination zones are combined together in a group. Using K-means algorithm, records within each group are the portioned in two or three clusters, based on their travel speeds. Each cluster represents a certain mode of travel; walking, public transportation or driving a private car.

A zonal inference model based on observed smart-card transactions for Santiago de Chile

The collection of origin–destination data for a city is an important but often costly task. This way, there is a need to develop more efficient and inexpensive methods of collecting information about citizens’ travel patterns. In this line, this paper presents a generic methodology that allows to infer the origin and destination zones for an observed trip between two public transport stops (i.e., bus stops or metro stations) using socio-economic, land use, and network information. The proposed zonal inference model follows a disaggregated Logit approach including size variables. The model enables the estimation of a zonal origin–destination matrix for a city, if trip information passively collected by a smart-card payment system is available (in form of a stop-to-stop matrix). The methodology is applied to the Santiago de Chile’s morning peak period, with the purpose of serving as input for a public transport planning computational tool. To estimate the model, information was gathered from different sources and processed into a unified framework; data included a survey conducted at public transport stops, land use information, and a stop-to-stop trip matrix. Additionally, a zonal system with 1176 zones was constructed for the city, including the definition of its access links and associated distances. Our results shows that, ceteris paribus, zones with high numbers of housing units have higher probabilities of being the origin of a morning peak trip. Likewise, health facilities, educational, residential, commercial, and offices centres have significant attraction powers during this period. In this sense, our model manages to capture the expected effects of land use on trip generation and attraction. This study has numerous policy implications, as the information obtained can be used to predict the impacts of changes in the public transport network (such as extending routes, relocating their stops, designing new routes or changing the fare structure). Further research is needed to improve the zonal inference formulation and origin–destination matrix estimation, mainly by including better cost measures, and dealing with survey and data limitations.

An investigation on the recreational value of Masouleh Village, Iran

ABSTRACTThe village of Masouleh is located at Fouman region in Guilan province, northern part of Iran. It is famous as an important tourism destination and recreational zone in Iran because of its numerous natural tourism attractions. Accordingly, the outdoor recreational value of Masouleh was estimated by the contingent valuation method (CVM). The logit model was estimated by the maximum likelihood approach to identify the effective variables on individuals' willingness to spend money for recreational programs. Questionnaire was the main tool for data collection, and structured interviews were conducted with 150 visitors in the spring of 2013. It was revealed that 80.1% of respondents were ready to spend money to use the studied region. In addition, it was found that variables including education, income, interest in nature, membership in environment-advocating associations and organizations, travelling time from departure to the region, non-nativeness and bid had significant effects on probability of respondents' willingness to pay (WTP), but variables such as age and family size were not statistically significant but had some expected signs. Maximum WTP for recreational use was estimated to be 26,049 IRR per person per visit.

Determination of Representative Path Set from Vehicle Trajectory Samples

AbstractThe representative path set between an origin and destination (OD) pair is useful for various transportation areas such as traffic assignment, travel information service, and traffic management. With the development of telecommunication technology, a car navigation system now collects trajectory-type data that can show the routes generated by drivers. Thus, the travel paths of individual travelers are available for the transportation study. Unfortunately, the availability of individual path data cannot guarantee direct application to the transportation model because it does not give us the significant path set between the OD pairs. Every driver in the real world has different starting and finishing points, yet these points are in a same origin and destination zone. In this study, a new methodology to determine the representative path set between an OD pair based on vehicle trajectory data is developed. This new model is tested with simulation data and compares various results according to clusteri...

Policy packages for modal shift and CO2 reduction in Lille, France

This paper proposes different policy scenarios to cut CO2 emissions caused by the urban mobility of passengers. More precisely, we compare the effects of the ‘direct tool’ of carbon tax, to a combination of ‘indirect tools’ – not originally aimed at reducing CO2 (i.e. congestion charging, parking charges and a reduction in public transport travel time) in terms of CO2 impacts through a change in the modal split. In our model, modal choices depend on individual characteristics, trip features (including the effects of policy tools), and land use at origin and destination zones. Personal “CO2 emissions budgets” resulting from the trips observed in the metropolitan area of Lille (France) in 2006 are calculated and compared to the situation related to the different policy scenarios. We find that an increase of 50% in parking charges combined with a cordon toll of €1.20 and a 10% travel time decrease in public transport services (made after recycling toll-revenues) is the winning scenario. The combined effects of all the policy scenarios are superior to their separate effects.

What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network

This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.BACKGROUND Transportation directly provides for the mobility of people and goods, while influencing land use patterns and economic activity, which in turn affect air quality, social equity, and investment decisions. Driven by the need to forecast future transportation demand and system performance, Manheim (1979) and Florian et al. (1988) introduced a transportation analysis framework for traffic forecasting using aggregated data that provide the basis for what is known as the four-step model: a process involving trip generation, then trip distribution and mode choice, followed by route choice. Aggregating demographic data at the zone level, the four-step model generates trip productions based on socioeconomic data (e.g., household counts by income and size) and trip attractions primarily based on jobs counts. The model then proportionally distributes trips between each origin and destination (OD) zone pair based on competing travel attractions and impedances, under the assumption that OD pairings with higher travel costs draw fewer trips. Trips between each OD pair are split among a variety of transportation modes, allocating trips to private vehicle, transit, or other