Origin And Destination Research Articles

Commuting Pattern Recognition of Industrial Parks Using Mobile Phone Signaling Data: A Case Study of Nanjing, China

As a novel industrial space to cope with global competition, industrial parks have gradually become important growth poles to promote regional development and provide a large number of employment opportunities. This study utilizes mobile phone signaling data to identify the commuting origins and destinations (OD) of different industrial parks in Nanjing while comparing the distribution of the working population, residential population, and commuting patterns across varying types and levels of industrial parks. The level of coordination of the employment–residential system in each park is quantified by calculating the resident commuting index (HSCi), employee commuting index (WSCi), and their coupling coordination degree. Additionally, geographic detectors are employed to identify the influencing factors and interaction effects that impact the employment–residential balance in industrial parks. Results show that industrial parks located in the central urban area attract more residential and working populations. The commuting volume of national and municipal as well as high-tech industrial parks is higher than other types of industrial parks. Most industrial parks experience more inward than outward commuting, and there is an uneven distribution of commuting flows, resulting in a network-like pattern of “central dense, peripheral sparse”. Various industrial parks exhibit a highly coupled job–housing system, and those with high HSCi tend to have high WSCi as well. The coupling coordination of industrial parks ranged from 0.16 to 0.93, with 13 being primary coordination or above and 3 being disordered. Industrial parks are classified into three types: employment-oriented, residential-oriented, and employment–residential balanced, with the residential-oriented type being predominant. The density of public transportation stops, park area, and land use mix are the primary factors affecting the employment–residential balance. Industrial parks with larger scale, better land allocation, and higher service facility levels are more likely to achieve coordination in the employment–residential system. Our work utilizes mobile signaling data to characterize the commuting patterns of industrial parks, providing insights for industrial park planning and promoting the integration of industry and city.

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

Disparities in resilience and recovery of ridesourcing usage during COVID-19

The COVID-19 pandemic has impacted ridesourcing services dramatically, but empirical research on disparities in the resilience and recovery of ridesourcing has been scarce. To address this literature gap, we used ridesourcing trip data in Chicago to create two time series: one for Census tract-level ridesourcing usage (including pickups and dropoffs) and the other for linkages between origin and destination (OD) pairs. We performed time-series clustering analyses that integrated manifold learning and Gaussian Mixture Modeling to optimize the number of clusters for high-dimensional time-series data. The tract-level usage can be grouped into three clusters, and the OD-pair linkages can be grouped into six clusters. We examined the spatial patterns of the tract-level usage clusters and the OD-pair linkage clusters. Furthermore, we estimated a multinomial logit regression model to examine the relationships between clusters and land use, built environment, and sociodemographic factors. Our results suggested that the share of residential land use had a positive association with high resilience and fast recovery of ridesourcing usage. Limited transportation accessibility and a lack of alternative transportation modes were also associated with high resilience and fast recovery of ridesourcing usage. Trips that linked dense employment centers were less likely to be made during the pandemic. Census tracts with a greater share of minorities or a higher poverty rate tended to generate more ridesourcing trips during the pandemic.

Optimizing Hydrogen Fueling Infrastructure Plans on Freight Corridors for Heavy-Duty Fuel Cell Electric Vehicles

<div>The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen-based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This article introduces the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy-duty (HD) fuel cell electric commercial vehicles (FCEV). This constraint-based optimization model considers existing fueling locations, regional-specific vehicle fuel economy and weight, vehicle origin and destination (O-D), and vehicle volume by class and infrastructure costs to characterize in-mission refueling requirements for a given freight corridor. The authors applied this framework to determine the ideal public access locations for hydrogen refueling (constrained by existing fueling stations), the minimal viable cost to deploy sufficient hydrogen fuel dispensers, and associated equipment, to accommodate a growing population of hydrogen fuel cell trucks. The framework discussed in this article can be expanded and applied to a larger interstate system, expanded regional corridor, or other transportation network. This article is the third in a series of papers that defined the model development to optimize a national hydrogen refueling infrastructure ecosystem for HD commercial vehicles.</div>

Rationale for governance and effective guidelines towards attainment of shared e-scooter micromobility benefits in Windsor, Ontario

The paper evaluates e-scooter trips using six-month (May to October 2021) pilot study data in Windsor. It employs exploratory and kernel density analysis to gain insight into spatiotemporal e-scooter trip dimensions in the city. Also, it uses regression mediation analysis to determine the effect of population and household densities on the relationship between average household income and the trips generated from Wards in the city. In total, 114,071 e-scooter trips were used for disaggregated and aggregated analysis. At a disaggregated level, exploratory studies showed that e-scooter use generally starts around 10:00 am. Depending on the weekdays, e-scooters' trip peak between 7:00 pm and midnight and are primarily used for short distances, duration, and at lower speeds. Across the study months, trips gradually decreased, but compared to fall months, spring and summer months have higher numbers of rides. Increased trips around weekends, late-night peak riding, and identical kernel density hotspots of the trip starts and ends locations around a downtown park in the city suggest e-scooter might be replacing walking or cycling. The study highlights e-scooter use for recreational purposes and safety concerns with late night riding of e-scooters in the city. Further, aggregated origin and destination (O-D) of e-scooter trips revealed that most trips start and end within the same Wards and occasionally in neighboring Wards that shared similar boundaries. This served as the basis for mediation analysis for understanding the influence of population and/or household density on the relationship between income and trips originating from Wards. The results from the mediation analysis showed that the average household income plays a more significant role in e-scooter ridership in Windsor. Thus, the study recommends effective e-scooter integration and governance within the city for the sustainable benefits of e-scooters to be derived. It highlights the need to adopt lessons derived from other countries that fully implement e-scooter micromobility modes.

Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network

Online car-hailing has become an indispensable transportation means for residents. The short-term origin and destination (OD) prediction of online car-hailing trips is conducive to understanding the inflow and outflow of online car-hailing trips in a region and provides data support for the delivery and scheduling of vehicles. Accordingly, this article takes the data of car-hailing trips in the central area of Haikou, China, as the research data; makes an in-depth analysis of the regularities of car-hailing trips; and divides the central area of Haikou into 84 grids with a length of 3 km. This article constructs three adjacency matrices, Am01, Sam, and Amn, to reflect the complex spatial relationships of the OD matrixes of online car-hailing from different perspectives. Then, a model, based on the graph convolutional network (GCN) and gated recurrent unit, denoted as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal GCN</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T-GCN</i> ), is introduced for the grid-based short-term OD prediction. The case study in Haikou shows that T-GCNs based on the three adjacency matrices are better than other models, wherein the Amn-based T-GCN is more consistent with the OD flows’ spatial relationship, achieves the best prediction performance, and shows that there exists a proportional relationship between flows on different OD pairs. The application of the research results is beneficial for the car-hailing platform to perform the dynamic scheduling of vehicles in advance, further improving the operating efficiency and reducing the waiting time of passengers so as to effectively alleviate the problem of the imbalance between the supply and demand of online car-hailing travel.

On the joint network equilibrium of parking and travel choices under mixed traffic of shared and private autonomous vehicles

This paper investigates the joint network equilibrium of parking and travel route choices in the future mobility paradigm with mixed traffic of private and shared autonomous vehicles. Specifically, we consider that private autonomous vehicle (PAV) travelers need to make both route and parking choices though the vehicle can drive itself to parking after dropping off the traveler. The origin–destination-based shared autonomous vehicle (OD-SAV) ride service allows multiple travelers with a common origin and destination (OD) pair to share the same vehicle, while the routing of OD-SAVs is determined by the operator. In this context, a bi-level model is developed which optimizes the OD-SAV service fare and OD-SAV flow in the upper-level, and specifies the travel demands, route and parking choices, and network traffic equilibrium in the lower-level. In particular, PAV travelers choose their route and parking location to minimize their own travel time or cost, while the routing of OD-SAVs is subject to the decision of the operator. The OD-SAVs controlled by the operator may minimize each vehicle’s travel time (user equilibrium, ‘UE’) or minimize the total travel time of all OD-SAVs operated by the operator (Cournot-Nash equilibrium, ‘CN’). The joint equilibrium of travel and parking under either UE or CN routing for OD-SAVs can be modeled as a Variational Inequalities (VI) problem. The uniqueness/non-uniqueness properties of the joint network equilibrium are investigated. Moreover, we examine the OD-SAV service operator’s optimal operation decisions subject to the lower-level network equilibrium. Solution approaches are introduced to solve the joint equilibrium and the proposed bi-level model. Numerical studies are conducted to illustrate the model and analytical results, and also to provide further understanding.

A System Optimization Approach for Trains’ Operation Plan with a Time Flexible Pricing Strategy for High-Speed Rail Corridors

Optimizing the train plan for high-speed rail systems should consider both the passengers’ demands and enterprise’s benefits. The choice of the departure time period is the most important factor affecting the passenger demand distribution. In this paper, the optimization problem of a train operation plan based on time period preference is studied for a high-speed rail corridor. First, according to the travel process of the passengers, the extended service network for a high-speed rail system is established. The main factors that influence the passengers’ travel choices are analyzed, and the departure time period preference, stop time and flexible pricing strategy based on the time period preference are put forward. The generalized travel cost function, including the convenience, ticket fare and stop time costs, is constructed, and a two-level programming model is established based on the function. The upper-level planning model is formulated as a mixed 0–1 programming problem that aims at maximizing the revenue of the railway enterprise. It is mainly constrained by passenger travel demand and solved by improved genetic algorithms. The lower-level model is a user equilibrium (UE) model. The Frank–Wolfe algorithm is used to allocate multiple groups of OD (origin and destination) passenger flows to each train so that the generalized travel expenses of all the passengers with the same OD are minimized and equal. Finally, the train operation plan is solved based on the Lan-xi (Lanzhou–Xi’an) high-speed rail data, and the validity of both the model and algorithm is verified.

Traffic Path Recommendation Model based on a Weighted Sum of Extracted Parameter

A path recommendation for vehicular traffic is important task of traffic analysis. It is a challenging problem for researchers to extract all paths and recommend the shortest path between Origin and Destination (OD) pairs. This paper comes up with a model which is established on the weighted sum of selected link references to recommend a path for OD pairs. First, to maintain spatial dependence between link references, a vehicular traffic network of roads is proposed as a rectangular coordinate system. The algorithm based on K-means and smoothing is introduced to select link references across OD pairs. A distance aggregation algorithm is proposed to evaluate all possible paths across an OD pair. Finally, out of overwhelming paths, the algorithm recommends the shortest distance path across an OD pair. Our proposed model effectively selects the link references and gets an overall shortest path recommendation. The proposed model analyzes the non-Euclidean distance of selected link references. Our experimental analysis shows that on an average, the first four link predictions lead to 77.37% distance coverage for the recommended path.

Optimization of multistage timeliness transit consolidation problem using adaptive-weighted genetic algorithm.

Cargo consolidation is becoming a crucial part of international transportation and changing the customer consumption patterns of the international community. Poor connections between different operations and the delay of international express have motivated sellers and logistics organizers to put timeliness first in international multimodal transport, especially during the COVID-19 epidemic. However, for cargo with small quality and multiple batches, designing an efficient consolidation network presents a set of unique challenges, including the coupling of multiple origins and destinations (ODs), and fully utilizing the capacity of the container. We defined a multistage timeliness transit consolidation problem to decouple the multiple ODs of the logistics resource. By solving this problem, we can increase the connectivity between different phases and make full use of the container. To make this systematic multistage transit consolidation more flexible, we proposed a two-stage adaptive-weighted genetic algorithm that mainly focuses on the edge area of the Pareto front space and the diversity of the population. Computational experiments indicate that the correlation between parameters has certain regular trends, and appropriate parameter settings can lead to more satisfactory results. We also confirm that the pandemic has a giant influence on the market share of different transportation modes. Moreover, the comparison with other approaches demonstrates the feasibility and effectiveness of the proposed method.

Statistical Analysis of Train Operation and Passenger Distribution Based on Real Records: A Case Study of Wuhan-Guangzhou HSR

This paper summarizes the results of an effort aimed at improving train operation schedules on Wuhan-Guangzhou high-speed railway (WG-HSR). The real-record train operation and passenger tickets-booking records of WG-HSR are used for statistical analysis on the train service quality and passenger distribution. More specifically, the train service frequency and interval time at each station are analyzed. Based on this, the temporal and spatial distribution of capacity utilization in each section are investigated. In order to get a holistic view of passenger flow characteristics, the passenger volume during different time periods and between several origin and destination (OD) pairs are investigated to characterize travellers’ spatial-temporal preferences. The passenger distributions on some long-distance trains are shown to get the number and proportion of cross-line passengers travelling on the WG-HSR. Moreover, for a better understanding of the seat capacity utilization of trains, the load rates of trains in various sections and time periods are investigated. Specifically, the relationship between the average load rate of trains and trains’ running distance is explored, finding that the longer the non-cross-line train travels is, the higher the average load rate is. This study provides insightful findings that help understanding HSR operation and conducting further research.

Integrating shared autonomous vehicles in Last-Mile public transportation

While public transportation (PT) plays a critical role in urban mobility, the discomfort and the disutility of the last-mile trips make PT unattractive. We study the feasibility of shared autonomous vehicles (AV) as an on-demand alternative to fixed-route buses for last-mile travel. To this end, the bus routes that operate along the edges of the PT network in Bursa, Turkey are selected. The origin and destination (OD) pairs of the trips made on these bus routes are inferred at an individual level. The vehicle routing problem is then formulated by considering the proposed transportation service. Several solution methods are developed for both solution construction and solution improvement phases of the problem. To assess solution methods with real-world data, an agent-based simulation framework is built. It is found that the solution methods successfullysolved a dynamic problem. The results show that the integration of PT using AVs is well-suited to improve the service quality and energy efficiency in the last-mile mobility.

Optimizing Long Term Hydrogen Fueling Infrastructure Plans on Freight Corridors for Heavy Duty Fuel Cell Electric Vehicles

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The development of a future hydrogen energy economy will require the development of several hydrogen market and industry segments including a hydrogen based commercial freight transportation ecosystem. For a sustainable freight transportation ecosystem, the supporting fueling infrastructure and the associated vehicle powertrains making use of hydrogen fuel will need to be co-established. This paper develops a long-term plan for refueling infrastructure deployment using the OR-AGENT (Optimal Regional Architecture Generation for Electrified National Transportation) tool developed at the Oak Ridge National Laboratory, which has been used to optimize the hydrogen refueling infrastructure requirements on the I-75 corridor for heavy duty (HD) fuel cell electric commercial vehicles (FCEV). This constraint-based optimization model considers existing fueling locations, regional specific vehicle fuel economy and weight, vehicle origin and destination (OD), vehicle volume by class and infrastructure costs to characterize in-mission refueling requirements for a given freight corridor. The authors applied this framework to determine the ideal long term public access locations for hydrogen refueling (constrained by existing fueling stations and dispensing technology), the minimal viable cost to deploy sufficient hydrogen fuel dispensers, and associated equipment, to accommodate a growing population of hydrogen fuel cell trucks. The framework discussed in this paper can be expanded and applied to additional electrified powertrains as well as a larger interstate system, expanded regional corridor, or other transportation networks.&lt;/div&gt;&lt;/div&gt;

Indicadores de mobilidade urbana na RMSP a partir da pesquisa OD-Metrô

Abstract The results of the 2017 Origin and Destination (OD) Metro survey for the Metropolitan Region of São Paulo (acronym in Portuguese: RMSP) were published in 2019. Through a spatial analysis of open data from the OD survey, this article aims to identify characteristics of the daily commuting performed by RMSP residents, investigating spatial patterns of travel time, mode choice, motorization rate, mobility and immobility index, considering population distribution according to different income ranges and disaggregated by OD zone. The 2017 results are compared to those of 2007 to investigate what changed and what remained during the decade.

Tripartite Graph Aided Tensor Completion For Sparse Network Measurement

Network measurements provide critical inputs for a wide range of network management. Existing network-wide monitoring methods face the challenge of incurring a high measurement cost. Some recent studies show that network-wide measurement data such as end-to-end latency and flow traffic, have hidden spatio-temporal correlations and thus low-rank features. Taking advantage of the low-rank feature, enlightened by tensor model's strong capability of information representation and extracting, this paper studies a novel sparse measurement scheduling problem which selects a proportion of Origin and Destination (OD) pairs to take measurements in the future time slots, while ensuring the data of the remaining un-measured OD pairs be accurately inferred through tensor completion. It is challenging to find the optimal sampling points (OD pairs) without knowing the structure of the future data and also infer the un-measured data in the presence of noise in the measurement samples. To conquer the challenges, we propose several techniques: a tripartite graph to illustrate the relationship between sample locations and tensor factorization, a graph-based sample selection algorithm, and a graph-based robust tensor completion algorithm. We have conducted extensive experiments based on two real network latency monitoring traces (PlanetLab and Harvard) and two other network monitoring traces (including a traffic trace Abilene and a throughput trace WS-Dream). Our results demonstrate that, even with a sampling ratio of less than 5%, our scheme can accurately obtain the complete network-wide monitoring data by inferring the missing ones based on the samples taken. To achieve similar recovery performance, the best peer tensor completion algorithm needs a significantly larger number of samples, with the sampling ratio up to 25-150 times ours.

Relationship analysis of short-term origin–destination prediction performance and spatiotemporal characteristics in urban rail transit

Accurately predicting passengers’ origin and destination (OD) demand and analyzing their spatiotemporal characteristics are the key to efficient operation and management of urban rail transit. While obtaining these spatiotemporal characteristics and thus making a short-term OD prediction are a big challenge for a model due to its high dimensionality and uncertainty. This paper proposes a pattern match algorithm based on t-distribution stochastic neighbor embedding and K nearest neighbors (TSNE-KNN) to promote the prediction performance and introduces similarity indicators to explore these features of OD flow and their relationship with the forecasting performance. Analysis of automatic fare collection data of Beijing rail transit shows that the TSNE-KNN model is superior to other state-of-the-art approaches, even including deep neural network models, and the similarity, which is affected by the functional attributes of station, the surrounding land use attributes and the degree of development of the road network, can be a universal indicator to indirectly reflect the time–space properties of the OD flow. It is found that as the similarity of daily OD flows decreases, the performance of short-term OD prediction of decreases, and rail transit stations are gradually shifting from the periphery to the center of the city.

Utilizing Wi-Fi Sensing and an Optimized Radius Algorithm to Count Passengers with Transfers to Enhance Bus Transit O-D Matrix

The origin and destination (O-D) of public transit passengers are important for the planning and operation of the transit system. However, only 46% of public transit agencies have a smart card system in the US, and most of them require an entry-only tap, which prohibits identifying passenger destinations unless utilizing an estimation model. Therefore, there is a need for a cost-effective and automated solution to facilitate the majority of the US transit agencies in recognizing the origin and destination of passengers as well as capturing passenger transfers. This paper created a novel algorithm for transit agencies to count passengers with the consideration of transfers using a cost-effective Wi-Fi sensing–based approach. Two pilot studies were conducted in the city of Louisville, Kentucky on three different bus routes to explore the feasibility of the method. A Wi-Fi detector was installed in the bus to detect passengers, and a manual counting was performed to be used as ground truth data. After data collection, the proposed algorithms were applied to optimize the detection radius and to eventually find origins, destinations, and transfers. Analysis revealed that the proposed Wi-Fi–based approach is capable of recognizing 78.7% of the total passengers as well as detecting their boarding and alighting activities. The paper demonstrates the ability of the proposed method to detect passengers with a reasonable detection rate by using Wi-Fi technology on bus routes, which makes it feasible for transit agencies to conduct frequent and low-cost network-level passenger O-D studies.

A Regional View of Passenger Air Link Evolution in Brazil

Passenger flows among cities, at both the domestic and international levels and in different countries and regions, have been studied for forecasting purposes. This paper seeks not a forecasting mechanism, but to understand, by analyzing passenger origins and destinations by Brazilian sub-region, how Brazil’s domestic air passenger network links have evolved. Using income, population, and fare price as inputs, and seats sold as output, air link performance is examined by data envelopment analysis to discuss the regional link of domestic passenger traffic in Brazil and its dynamics, considering two specific years. The findings indicate that, although the highest passenger flow density is concentrated in Brazil’s Southeast region, performance by emerging origins and destinations (O-Ds), such as those connecting the Northeast, display more substantial strength indices and advances (Malmquist analysis). The analysis of specific links was also important, which showed that important Brazilian airports are not necessarily more competent in generating trips. The Catch-Up indicator for innovation reveals the weak point in Brazil’s air transport network. Although some airports enjoy strong networkability, they do not correspond in passenger origin or destination strength.

OD-Based Partition Technique to Improve Arterial Signal Coordination Using Connected Vehicle Data

Maximizing two-way arterial bands often plays a critical role in signal coordination along urban arterials. With an increasing number of signals involved, the arterial bands tend to shrink to some extent resulting in inefficient arterial signal coordination, and not all vehicles fully take advantage of the arterial bands to travel through the entire corridor. In response to that, system partition is a technique for handling arterials with many signals. Rather than designing end-to-end signal coordination, efficient arterial signal coordination is highly reliant on traffic origin and destination (OD) patterns on the arterial, which have been difficult to obtain using conventional data collection methods. The emerging big data sources, such as connected vehicles, provide great potential to gather such invaluable OD information for improving arterial signal coordination. This research proposes an easy-to-implement OD-based partition technique to improve arterial signal coordination by utilizing vehicle trajectory data automatically collected from connected vehicles. The proposed signal timing technique was tested using an arterial with 17 signalized intersections in Orange County, California. The results demonstrated that the OD-based partition technique improved the arterial average travel speed by 2.7% and 12.1% for the eastbound and westbound directions, respectively. At the same time, the proposed technique shortened the arterial average travel time by 2.6% and 11.1% for the eastbound and westbound directions, respectively. The total travel time was shortened both for the main-street and side-street major traffic flows.

Estimating freight production/attraction using nighttime satellite imagery

An appropriate understanding and accurate estimation of zonal freight production (FP) and attraction (FA) volume is one of the most important topics and the initial step in developing the origin and destination (OD) matrix. In general, zonal FP and FA are difficult to be obtained as observed data, particularly in developing countries, owing to the lack of large-scale freight surveys. Thus, several proxy indicators are often used for estimating FP and FA, such as the gross regional product (GRP), population, and the number of establishments. However, these indicators do not often estimate the zonal FP and FA accurately and are even not available readily. The nighttime light intensity (NLI) data that is human-induced light emission data and one of the open alternative data, possibly improves the accuracy of FP and FA estimation. However, few studies have examined the relationship between NLI and FP/FA. Therefore, the objectives of this study are to examine whether NLI can be a significant indicator for estimating zonal FP and FA relative to other socio-economic indicators and to examine the types of NLIs that can accurately estimate the FP and FA through a case study in Japan. As a result of the analysis, NLI data are identified as a significant variable for the estimation of FP and FA among various socio-economic variables. Although some of the indicators related to secondary industries provide higher accuracy, NLI exhibits better estimates than the popular indicators such as GRP and population. In addition, we propose three types of total NLI that multiply the unit NLI by the zonal area and this generates higher accuracy of unit NLI, the average light intensity of all pixels in a zone. We have also found that the use of habitable area for the estimation of total NLI provides the highest accuracy among the four types of NLIs including unit NLI. Through the analysis, NLI data is sufficiently applicable for estimating zonal FP and FA, relative to other important socio-economic indicators, although space transferability should be examined in other study areas. These findings could help estimating FP/FA particularly in countries and regions with limited statistical data available.