City Pairs Research Articles

Modeling interurban mentioning relationships in the U.S. Twitter network using geo-hashtags

Twitter users mention cities in the context of tourist attractions or events, such as protests or games, thus forming a network between cities from which they tweet and cities that they tweet about. This study tackles the challenge of explaining why users tweet about cities outside of their own by analyzing an underlying network of city mentions on Twitter. It applies graph theory as well as various measures of network connectivity such as indegree, hub score, and authority score to examine the prominence of individual cities in the Twitter landscape and the connection patterns between cities. Closely related to communication ties is the sentiment of tweets about other cities, which can be extracted from the text of tweets that contain geohashtags, i.e., hashtags with names of other cities. The effect of distance between cities on user sentiments towards cities will be explored. Furthermore, Quadratic Assignment Procedure (QAP) network regression will be used to build a general socio-demographic and geographic model that helps to identify which characteristics of city pairs, e.g. separation distance, or similarity in employment data or population, increase or decrease the likelihood of mentions between those cities. Findings show that distance and network size (compactness) are major determinants in communication ties between cities. City popularity, when measured by indegree, follows a power-law distribution, and is closely tied to population, GDP, or visitor numbers. Larger cities reveal a higher percentage of self-mentions than smaller cities, showing the high level of attention these metropolitan areas attract from Twitter users due to the many opportunities, events, and sights offered. Future research in the field of analysis of geotagged tweets can further extend the network regression model with new covariates.

Evaluation of Coordinate Points Consistency in Medieval Geographical Manuscripts (the case of Ibn Sa'id's Data on the Southern and Eastern Mediterranean)

The article develops a technique for analyzing mathematical data (longitude and latitude) characterizing the location of cities in medieval Arabic geographical manuscripts. The calculations made at the coordinates of 19 points given by the Arab-Spanish traveler of the 13th century Ibn Sa'id. These data are taken from his text and from the book of Abu-l-Fida referring to Ibn Sa'id. With the help of the Python - PostgreSQL technological stack for each pair of cities a set of spheroids was determined that fit for placement them in the corresponding coordinates. Then the options for intersecting the sets in all possible combinations are analyzed. The consistent exclusion of all possible combinations of cities from the entire set allows these cities to be arranged according to the degree of consistency of coordinates for any number of withdrawn points. The results tell us that most of the coordinates were not the result of astronomical observations. One of the tasks was to test a hypothesis that some of the cities the coordinates of which were determined more accurately served as “reference” points serving the basis for calculating coordinates of the rest of the cities. Complete consistency of coordinates was achieved with the exclusion of 14 cities from the set in the first version and 13 cities in the second one. However, the geography of the rest cities in Ibn Sa’id’s version does not support the “reference points” hypothesis. Abu-l-Fida’s version can boast a result that is more close to the hypothes, but to prove it one has to solve the problem of data origin used by Abu-l-Fida to correct coordinates. The technique described can be applied to any other set of coordinates of ancient geographical descriptions to evaluate the degree of their consistency and search for “reference” points.

Savings in per-passenger CO2 emissions using rail rather than air travel in the northeastern U.S.

ABSTRACT Individuals and institutions seeking to reduce travel-related carbon dioxide (CO2) emissions by changing travel modes need information on the amount of CO2 that can be saved by rail travel rather than air travel. This study uses flight emissions data from the International Civil Aviation Organization (ICAO) to estimate average per-passenger CO2 emissions saved by using rail travel between selected city pairs in the northeastern U.S. Trend lines are developed from the ICAO data for different aircraft types to facilitate comparison with CO2 emissions from rail travel. Separate rail emission factors are calculated for portions of Amtrak’s system operating electric and diesel locomotives. An adjustment factor is estimated to account for longer rail distances than flight distances. Results show rail travel has generally lower CO2 emissions than air travel, with substantially lower emissions for electrified segments of the Amtrak system. At flight distances of over 700 miles, air travel using single-aisle jets can have lower per-passenger CO2 emissions compared to diesel-powered rail travel, accounting for the longer distances by rail. Implications: Savings in per-passenger CO2 emissions using rail rather than air travel in the northeastern U.S. Travel by rail in the northeastern U.S. results in lower CO2 emissions compared to travel by air between the same city pairs using existing airline and passenger rail infrastructure. Savings are higher for cities connected by electrified rail.

Prediction of aircraft engine emissions using ADS-B flight data

ABSTRACTReal-time flight data from the Automatic Dependent Surveillance–Broadcast (ADS-B) has been integrated, through a data interface, with a flight performance computer program to predict aviation emissions at altitude. The ADS-B, along with data from Mode-S, are then used to ‘fly’ selected long-range aircraft models (Airbus A380-841, A330-343 and A350-900) and one turboprop (ATR72). Over 2,500 flight trajectories have been processed to demonstrate the integration between databases and software systems. Emissions are calculated for altitudes greater than 3,000 feet (609m) and exclude landing and take-off cycles. This proof of concept fills a gap in the aviation emissions inventories, since it uses real-time flights and produces estimates at a very granular level. It can be used to analyse emissions of gases such as carbon dioxide ($\mathrm{CO}_2$), carbon monoxide (CO), nitrogen oxides ($\mathrm{NO}_x$) and water vapour on a specific route (city pair), for a specific aircraft, for an entire fleet, or on a seasonal basis. It is shown how$\mathrm{NO}_x$and water vapour emissions concentrate around tropospheric altitudes only for long-range flights, and that the cruise range is the biggest discriminator in the absolute value of these and other exhaust emissions.

МОДЕЛИРОВАНИЕ ПРОСТРАНСТВЕННОГО РАСПРЕДЕЛЕНИЯ МЕЖДУГОРОДНЫХ АВТОМОБИЛЬНЫХ ПОЕЗДОК НА ОСНОВЕ ДАННЫХ СЕРВИСОВ КАРПУЛИНГА

The paper considers a spatial distribution of automobile traffic between cities of the Sverdlovsk region. For its description the authors have applied a correspondence matrix represented by the number of trips between each pair of cities. A calculation of elements of correspondence matrix is carried out by known total departures and arrivals with the use of a transport gravity model. As initial data for calibration of the gravity model the authors have used the information gained on the online-service for searching fellow travellers (carpooling). The paper considers the gravity model with an exponential gravity function. At calibration the authors have used the Hyman method. As a result, the authors have carried out an analysis of the observed correspondence matrix and a calculation of the modeling one. A comparison of absolute deviations of element values for the modeling and the observed matrices and a comparison of the modeling and the observed distributions of trip length have showed that the gravity model with the exponential gravity function adequately represents the spatial distribution of trips presents on the online-service for searching fellow travellers.

Proximity and Knowledge Spillovers: Evidence from the Introduction of New Airline Routes

This paper studies the causal relationship between proximity and knowledge diffusion by exploiting sudden changes in travel time following the introduction of new flight routes. We find that decreasing travel time between U.S. cities by 20% increases knowledge flow by 0.5%, which corresponds to an increase of over 15,000 citations at the aggregate level. Importantly, this effect is driven mainly by the rise in knowledge spillovers across firm boundaries instead of through inventors’ mobility or firms’ spatial expansion. The effects are stronger in rapidly-evolving technological fields and city pairs with higher absorptive capacity, but are smaller during the age of the Internet, as alternative means of information transfer start to emerge.

Why We Can Expect More Competition among European Low Cost Carriers Post-Pandemic

This study considers the network development of the three largest European Low Cost Carriers (LCCs) easyJet, Ryanair and Wizz Air during the pre-pandemic period and the pandemic period. Network developments are characterized in terms of the route numbers, city-pair numbers, frequencies, market sizes in terms of GDP, and network overlaps. The LCC flight networks maintained in the pre-pandemic period and the pandemic period and especially the peak month August are compared to derive insights on the intensity of LCC competition in Europe. Competition is considered as most intense on routes or origin-destination city pairs which are maintained in parallel by airlines. The results indicate that LCC network overlaps were growing substantially at the end of the pre-pandemic period and especially during the pandemic period. Wizz Air increasingly expanded their network and/or diverted flights into markets that had already been served by Ryanair and easyJet. The development of network overlaps during the pandemic is considered as an indicator for the intensity of the competition among LCCs in Europe after the pandemic.

Air Pollution Characteristics and Health Risks in the Yangtze River Economic Belt, China during Winter.

The air pollution characteristics of six ambient criteria pollutants, including particulate matter (PM) and trace gases, in 29 typical cities across the Yangtze River Economic Belt (YREB) from December 2017 to February 2018 are analyzed. The overall average mass concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 are 73, 104, 16, 1100, 47, and 62 µg/m3, respectively. PM2.5, PM10, and NO2 are the dominant major pollutants to poor air quality, with nearly 83%, 86%, and 59%, exceeding the Chinese Ambient Air Quality Standard Grade I. The situation of PM pollution in the middle and lower reaches is more serious than that in the upper reaches, and the north bank is more severe than the south bank of the Yangtze River. Strong positive spatial correlations for PM concentrations between city pairs within 300 km is frequently observed. NO2 pollution is primarily concentrated in the Suzhou-Wuxi-Changzhou urban agglomeration and surrounding areas. The health risks are assessed by the comparison of the classification of air pollution levels with three approaches: air quality index (AQI), aggregate AQI (AAQI), and health risk-based AQI (HAQI). When the AQI values escalate, the air pollution classifications based on the AAQI and HAQI values become more serious. The HAQI approach can better report the comprehensive health effects from multipollutant air pollution. The population-weighted HAQI data in the winter exhibit that 50%, 70%, and 80% of the population in the upstream, midstream, and downstream of the YREB are exposed to polluted air (HAQI > 100). The current air pollution status in YREB needs more effective efforts to improve the air quality.

Labor costs and the adoption of robots in China

We study the effects of higher labor costs on the adoption of industrial robots by Chinese firms. We use the number of robots imported by firms from the customs data to measure the adoption of robots. To address endogeneity concerns, we focus on the effects of higher minimum wages from contiguous city pairs that straddle provincial borders. We find negligible effects of minimum wages on firms’ robot adoption over 2004–2007, but much larger and statistically significant effects over 2008–2012. For a 10% increase in the minimum wage over 2008–2012, the probability of a firm adopting robots increases by 0.11 percentage points, compared to an average adoption probability of 0.19% over the same period. The effects of higher minimum wages on robot adoption are stronger for firms with higher productivity, those in the coastal region, those with private ownership, and those from skilled-labor-intensive industries.

Reliability Optimization of a Railway Network

With the increase of the railway operating mileage, the railway network is becoming more and more complicated. We expect to build more railway lines to offer the possibility to offer more high quality service for the passengers, while the investment is often limited. Therefore, it is very important to decide the pairs of cities to add new railway lines under the condition of limited construction investment in order to optimize the railway line network to maximize the reliability of the railway network to deal with the railway passenger transport task under emergency conditions. In this paper, we firstly define the reliability of the railway networks based on probability theory by analyzing three minor cases. Then we construct a reliability optimization model for the railway network to solve the problem, expecting to enhance the railway network with the limited investment. The goal is to make an optimal decision when choosing where to add new railway lines to maximize the reliability of the whole railway network, taking the construction investment as the main constraint, which is turned to the building mileage limit. A computing case is presented based on the railway network of Shandong Province, China. The computing results prove the effectiveness of the model and the efficiency of the algorithm. The approach presented in this paper can provide a reference for the railway investors and builders to make an optimal decision.

Enhanced cruise range prediction for narrow-body turbofan commercial aircraft based on QAR data

ABSTRACTAircraft performance parameters play a critical role in maintaining economic and environmental sustainability in aviation. Furthermore, the ability to calculate aircraft performance parameters accurately for the cruise range contributes to aviation in areas such as the preliminary design of aircraft and air traffic management. This study is focused on cruise range performance, as this is critical to both the evaluation and understanding of the economic and environmental impacts of commercial aircraft. Quick Access Recorders (QAR) data were used for more accurate analysis of the cruise range. The QAR data used in this study included 6,574 short-distance domestic flights by narrow-body turbofan commercial aircraft between 31 different city pairs. To obtain a more accurate cruise range equation, parameters affecting the cruise range performance were determined and studied. First, the drag polar model was improved to take the cambered profile, compressibility effects and cruise airspeeds of commercial aircraft into consideration using the real flight data. Second, Thrust-Specific Fuel Consumption (TSFC) models were compared and the most suitable one for the cruise phase was selected. After these steps, cruise range values were calculated using the Breguet range equation with these improved parameters. When the results of this enhanced range model were compared with the real flight data, the mean absolute percentage error (MAPE) was found to be 2.5% for all the Aircraft and Engine Type Groups (AETGs) considered in the data. This figure corresponds to a 7.9% smaller error than provided by previous range models based on simple parabolic drag polar and TSFC models. According to these results, the application of a simple parabolic drag polar and TSFC is not appropriate for cruise range calculations.

Climate-Optimized Trajectories and Robust Mitigation Potential: Flying ATM4E

Aviation can reduce its climate impact by controlling its CO2-emission and non-CO2 effects, e.g., aviation-induced contrail-cirrus and ozone caused by nitrogen oxide emissions. One option is the implementation of operational measures that aim to avoid those atmospheric regions that are in particular sensitive to non-CO2 aviation effects, e.g., where persistent contrails form. The quantitative estimates of mitigation potentials of such climate-optimized aircraft trajectories are required, when working towards sustainable aviation. The results are presented from a comprehensive modelling approach when aiming to identify such climate-optimized aircraft trajectories. The overall concept relies on a multi-dimensional environmental change function concept, which is capable of providing climate impact information to air traffic management (ATM). Estimates on overall climate impact reduction from a one-day case study are presented that rely on the best estimate for climate impact information. Specific weather situation that day, containing regions with high contrail impact, results in a potential reduction of total climate impact, by more than 40%, when considering CO2 and non-CO2 effects, associated with an increase of fuel by about 0.5%. The climate impact reduction per individual alternative trajectory shows a strong variation and, hence, also the mitigation potential for an analyzed city pair, depending on atmospheric characteristics along the flight corridor as well as flight altitude. The robustness of proposed climate-optimized trajectories is assessed by using a range of different climate metrics. A more sustainable ATM needs to integrate comprehensive environmental impacts and associated forecast uncertainties into route optimization in order to identify robust eco-efficient trajectories.

Impacts of high-speed rail on domestic air cargo traffic in China

Air cargo is vital to the modern supply chain because it provides efficient and timely delivery. In the past decade, the development of high-speed rail (HSR) in China has resulted in significant impacts on air cargo traffic, which has long been neglected by the existing studies. In this research, we quantify such impacts using HSR entry dummy variables and employ a panel regression approach to capture the impacts, specifically, the impacts on air cargo volumes and flight frequencies. We conduct a case study using quarterly air cargo traffic data in China, which contains 321 city pairs and spans between January 2011 and December 2017. We separately analyze the impacts on belly-hold cargo (carried by combi or passenger aircraft) traffic and freighter cargo (carried by freighters) traffic. We first capture the general impacts on belly-hold cargo traffic and freighter cargo traffic for all city pairs. Then, to quantify the distance level impacts on belly-hold cargo traffic, we categorize city pairs into multiple groups according to their distance and apply the panel regression approach in each group. Our main results include: (1) The entry of HSR services reduces the belly-hold cargo volumes and flight frequencies by 21.9% and 19.4%, respectively; (2) The strongest impacts on belly-hold cargo volumes and flight frequencies are observed in the medium-haul group within a distance of 800–1300 km; (3) After the entry of HSR services, the cargo volumes and flight frequencies of freighter cargo generally increase by 22.3% and 20.9%, respectively; (4) The whole air cargo traffic in the measurement of cargo volumes and flight frequencies decreases after the entry of HSR, and the impacts on whole air cargo volumes are relatively smaller than the belly-hold cargo volumes in the short-haul (no more than 800 km) and long-haul (more than 1300 km) groups.

Subjective valuation of tangible and intangible heritage neighbourhood attributes

Using 23-year panel data of cross-border venture capital investment and global air network expansion as a natural experiment, this paper investigates the effect of air travel on cross-border capital mobility. We find international airlines have a significant facilitating effect on venture capital mobility. Based on the difference-in-differences estimate, for our research period one additional flight per day leads to an increase in VC investment between the city pair by 0.14 deals on average per year (1.08 times the mean value). The heterogeneous analysis indicates that, with more air connections, emerging industries and firms in earlier developmental stages receive more investment, and non-syndication investment increases more than syndication investment. In addition, we find the effect significantly increases with geographical distance: city pairs that are far away from each other experience a larger increase in VC flow after flight connections exist. Lastly, the evidence suggests that most of this increase in capital flows happens in wealthier cities. Connections between poor cities show little effect on VC flow. The paper presents evidence on the role of the global air network in reducing information transmission cost and raising the expectation for future growth potential. All results are shown to be robust using alternative VC investment measures and the regression discontinuity method.

The Integration of Border Regions in the European Union: A Model Approach

ABSTRACT This study analyzes the problems related to the integration of border regions in the EU. Existing literature on the subject presents a range of models that specify different types of border regions based on the intensity of their interactions and accounting for the causes of their development. It has been suggested that these models may help in evaluating the intensity of cross-border interaction, but such assumptions have not been empirically verified. Moreover, most of the presented models also fail to fully account for integration processes in the EU. Thus, the stages of cross-border integration defined in the literature seem insufficient. The current paper proposes a five-category model for border regions, listing the stages of integration between them: from regions that demonstrate no interaction whatsoever, to cross-border regions – covering at least two border regions – with the most intense interaction. The model has been empirically verified on the basis of selected Polish-German pairs of cities.

General Northern English. Exploring Regional Variation in the North of England With Machine Learning.

In this paper, we present a novel computational approach to the analysis of accent variation. The case study is dialect leveling in the North of England, manifested as reduction of accent variation across the North and emergence of General Northern English (GNE), a pan-regional standard accent associated with middle-class speakers. We investigated this instance of dialect leveling using random forest classification, with audio data from a crowd-sourced corpus of 105 urban, mostly highly-educated speakers from five northern UK cities: Leeds, Liverpool, Manchester, Newcastle upon Tyne, and Sheffield. We trained random forest models to identify individual northern cities from a sample of other northern accents, based on first two formant measurements of full vowel systems. We tested the models using unseen data. We relied on undersampling, bagging (bootstrap aggregation) and leave-one-out cross-validation to address some challenges associated with the data set, such as unbalanced data and relatively small sample size. The accuracy of classification provides us with a measure of relative similarity between different pairs of cities, while calculating conditional feature importance allows us to identify which input features (which vowels and which formants) have the largest influence in the prediction. We do find a considerable degree of leveling, especially between Manchester, Leeds and Sheffield, although some differences persist. The features that contribute to these differences most systematically are typically not the ones discussed in previous dialect descriptions. We propose that the most systematic regional features are also not salient, and as such, they serve as sociolinguistic regional indicators. We supplement the random forest results with a more traditional variationist description of by-city vowel systems, and we use both sources of evidence to inform a description of the vowels of General Northern English.

The emissions reduction potential of substituting short-haul flights with non-high-speed rail (NHSR): The case of Finland

Replacing short-haul flights with high-speed rail (HSR) has been widely discussed as one solution to mitigate the climate change impacts of aviation. However, although HSR can provide travel times similar to those provided by short-haul flights, and at lower emission levels, it also requires considerable investments in time and infrastructure to build. Instead, this study considers the feasibility of replacing short-haul flights with existing non-high-speed rail (NHSR). Our study is based in Finland, a country that has an extensive route network of short-haul flights but does not possess any HSR. We compared all 16 city pairs for which short-haul flights are offered with existing NHSR based on the total carbon dioxide equivalent emissions (CO2-eq) and real travel times from door-to-door. Two scenarios were developed based on the results, which suggest replacing all short-haul flights with NHSR in Finland. This would result in a 95% emissions reduction. In terms of travel times, NHSR could remain competitive against air travel on distances up to 400 km.

Experiment in Finding Look-Alike European Cities Using Urban Atlas Data

The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone.

Does China's high-speed rail development lead to regional disparities? A network perspective.

This research examines whether cities are getting more equally accessible and connected via high-speed rail (HSR) in China over the period from 2010 to 2015. Existing studies mainly use network centralities to describe the spatial pattern of HSR network without measuring the spatial disparity of these centralities, and most of them rely on the infrastructure network and thus fail to incorporate HSR service quality in the centrality measures. Using HSR timetable data, we incorporate both scheduled travel time and daily frequency of each origin-destination city pair into three centrality measures and further quantify their inequalities using Theil's T index. We find that as the HSR network expands, cities appear to be more equal in terms of accessibility, but their disparities in connectivity and transitivity depend on the dimensions of comparison. In general, although the difference between economic regions or between megalopolises has reduced, small/medium-sized cities not belonging to any major city cluster are further lagged behind in HSR development. The difference between core and non-core cities in the same megalopolises has decreased despite that non-core cities are increasingly relying on core cities to access other regions.

The development of long-haul low-cost networks in the North Atlantic airline market: An exploratory data approach

For almost two decades, discussion in the literature has continued regarding the viability of the long-haul low-cost carrier (LHLCC) business model in the North Atlantic market. From 2015 onward, LHLCC operations gained some steam, mainly due to the expansion of Norwegian's North Atlantic network. A detailed analysis of the traffic developments on the North Atlantic shows that LHLCCs have a tendency to operate on parallel routes while serving an aggregate North Atlantic network that is less seasonal than that of full-service network carriers (FSNCs). In addition, analysis of passenger flows shows that Norwegian's North Atlantic flights from its Scandinavian bases and, to a lesser extent, from London Gatwick carry substantial shares of transfer passengers. Furthermore, both in the North Atlantic market as a whole and in specific individual city pair markets there are signs that the entry of LHLCCs has a positive impact in terms of competition. Strategic responses by FSNC incumbents can be detected as well. For example, British Airways shows a larger increase in traffic (both in seats offered and in flight frequency) on routes contested by Norwegian than on North Atlantic routes not contested by Norwegian. Next to that, the British flag carrier is able to substantially undercut Norwegian's air fares on most of its contested routes. All in all, after more than a decade of EU-US and EU-Canada Open Skies agreements LHLCC operations on the North Atlantic are dominated by a single airline in financial dire straits. As a result, the long-term viability of the LHLCC business model on the North Atlantic is not a fait accompli yet.