scholarly journals Measuring when Uber behaves as a substitute or complement to transit: An examination of travel-time differences in Toronto

2019 ◽  
Author(s):  
Mischa Young ◽  
Jeff Allen ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Least Square ◽  
Wait Times ◽  
Ordinary Least Square ◽  
Logistic Regressions ◽  
The Difference ◽  
Transit Agencies ◽  
Using Data ◽  
The City

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize ride-hailing services as a substitute or complement to transit, and demonstrate the need to examine ride-hailing trips individually. Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the difference in travel-time between observed ride-hailing trips and their fastest transit alternative. Ordinary least square and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. We also find discrepancies in travel-time to be often caused by lengthy walk- and wait-times for transit or because of transfers. Our results further indicate that 30.61% of ride-hailing trips in our sample have transit alternatives of similar duration. These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable transit expectations. We also find that 26.87% of ride-hailing trips would take at least 30 minutes longer by transit. In light of these findings, we discuss recommendations for ride-hailing taxation structures.

scholarly journals Measuring when Uber behaves as a substitute or complement to transit: An examination of travel-time differences in Toronto

2019 ◽  
Author(s):  
Mischa Young ◽  
Jeff Allen ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Ordinary Least Squares ◽  
Gap Filling ◽  
Service Levels ◽  
Transit Service ◽  
Logistic Regressions ◽  
Transit Agencies ◽  
Using Data ◽  
The City

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize all ride-hailing services as a substitute or supplement to transit, and demonstrate the need to examine ride-hailing trips individually. Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the differences in travel-times between observed ride-hailing trips and their fastest transit alternatives. Ordinary least squares and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. Also, we find differences in travel-time often to be caused by transfers and lengthy walk- and wait-times for transit. Our results further indicate that 31% of ride-hailing trips in our sample have transit alternatives of similar duration (≤ 15 minute difference). These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable expectations of transit service levels. We also find that 27% of ride-hailing trips would take at least 30 minutes longer by transit, evidence for significant gap-filling opportunity of ride-hailing services. In light of these findings, we discuss recommendations for ride-hailing taxation structures.

scholarly journals Study of Selection of Bus Mode and Rail Train Surabaya – Malang

2020 ◽  
Vol 3 (2) ◽  
pp. 36-44
Author(s):  
Haris Muhammadun ◽  
Sindiah Bagus Mahendra Tama ◽  
Wateno Oetomo ◽  
Sri Wiwoho Mudjanarko
Keyword(s):  
Sensitivity Analysis ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Travel Time ◽  
Logistic Regression Analysis ◽  
Public Transportation ◽  
Travel Costs ◽  
The Difference ◽  
The City ◽  
Selection Of

The need for transportation facilities and infrastructure that is quite large in the city of Surabaya to the city of Malang is the impact of population growth and increased activity in the city. To support economic, social, trade and education activities between the two cities, public transportation facilities are needed that can meet the needs of the community in terms of comfort and safety. Fulfillment of comfort factors in the use of public transportation such as cheap travel costs, speed of travel time, and accuracy of operational schedules, is expected to increase the interest of travelers to use public vehicles more often than private vehicles. The analysis method used, among others, Descriptive Analysis aims to identify the characteristics of users of the Surabaya-Malang and bus modes, then the Logistic Regression Analysis and Binomial Logit Difference Analysis aim to Obtain a model of selection of bus and train modes in the Surabaya-Malang route, then proceed Sensitivity Analysis which aims to determine the sensitivity of the model of the response of the traveler in determining the choice if there is a change in each attribute of the trip. The results of logistic regression analysis and binomial logit difference, it can be concluded that, attribute / variable X2 (travel time) is the attribute / variable that most influences the mode selection. The sensitivity analysis results can be concluded as follows: Sensitivity to travel costs, the possibility of respondents prefer to use the train mode is greater than the bus. Sensitivity to time, the possibility of respondents prefer to use the train mode is greater than the bus. Sensitivity to the departure schedule (headway), the possibility of respondents choosing to use the train mode will be greater than the bus, if the difference in the headway is between 90-165 minutes. However, if the difference in headway is above 165 minutes, then the respondent will switch to choosing the bus mode.

scholarly journals The INFLUENCE OF PRODUCTION FACTORS TOWARD ECONOMIC GROWTH IN INDONESIA

2019 ◽  
Vol 4 (1) ◽  
pp. 44-46
Author(s):  
Wilson Bangun
Keyword(s):  
Economic Growth ◽  
Secondary Data ◽  
Least Square ◽  
Production Factor ◽  
Domestic Investment ◽  
Ordinary Least Square ◽  
Production Factors ◽  
The People ◽  
Data Production ◽  
Using Data

Economic growth as a mesurement and reflect of the people prosperity. Employee production factor have a better contribution if  to compare with capital and technology production factors on Indonesian economic growth. However, Indonesian workforce quality is lowest in ASEAN-5. The research methodology is using the Cobb-Douglas production function with the Ordinary Least Square (OLS), the using equation formulation: lnY = ln a + bi  +e. This research using data is secondary data: production factors using data of progressing of FDI and domestic investment, source of  the World Bank, 2004-2016; Employment is using data of progressing of Indonesia workforce,  sourced from the Biro Pusat Statistik Republik Indonesia, 2004-2016. The research results show that influence of the production factors toward Indonesia economic growth is strongly. This researchs aim to knowledge a large the contribution of production factors on Indonesian Economic growth.

scholarly journals ANALISIS PENGARUH INVESTASI SWASTA , PENGELUARAN PEMERINTAH, DAN PENDUDUK TERHADAP PERTUMBUHAN EKONOMI DI KOTA BITUNG

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yandiles Weya ◽  
Vecky A.J. Masinambow ◽  
Rosalina A.M. Koleangan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Economic Activity ◽  
Time Series Data ◽  
Private Investment ◽  
Least Square ◽  
Series Data ◽  
Ordinary Least Square ◽  
North Sulawesi ◽  
The City

ANALISIS PENGARUH INVESTASI SWASTA , PENGELUARAN PEMERINTAH, DAN PENDUDUK TERHADAP PERTUMBUHAN EKONOMI DI KOTA BITUNG Yandiles Weya, Vecky A.J. Masinambow, Rosalina A.M. Koleangan. Fakultas Ekonomi dan Bisnis, Magister Ilmu EkonomiUniversitas Sam Ratulangi, Manado ABSTRAKPada suatu periode perekonomian mengalami pertumbuhan negatif berarti kegiatan ekonomi pada periode tersebut mengalami penurunan. Kota Bitung periode tahun 2004-2014 mengalami pertumbuhan ekonomi yang fluktuasi. Adanya fluktuasi ini dapat dipengaruhi oleh investasi swasta, belanja langsung, dan penduduk Pertumbuhan ekonomi merupakan salah satu tolok ukur keberhasilan pembangunan ekonomi di suatu daerah. Pertumbuhan ekonomi mencerminkan kegiatan ekonomi. Pertumbuhan ekonomi dapat bernilai positif dan dapat pula bernilai negatif. Jika pada suatu periode perekonomian mengalami pertumbuhan positif berarti kegiatan ekonomi pada periode tersebut mengalami peningkatan. Sedangkan jikaTahun 2004-2014 yang bersumber dari Badan Pusat Statistik Provinsi Sulut dan Kota Bitung. Metode analisis yang digunakan adalah model ekonometrik regresi berganda double-log (log-log) dengan metode Ordinary Least Square (OLS). Penelitian ini bertujuan untuk mengetahui apakah perkembangan investasi swasta, belanja langsung, dan penduduk berpengaruh terhadap pertumbuhan ekonomi Kota Bitung. Data yang dipakai menggunakan data time series periodeHasil regresi model pertumbuhan ekonomi dengan persamaan regresinya yaitu  LPDRB  =  - 4,445    +  0.036 LINV  +  0.049 LBL  +  2,229 LPOP.  Dari hasil tersebutmenunjukkan perkembangan investasi swasta, belanja langsung dan penduduk berpengaruh positif dan signifikan terhadap pertumbuhan ekonomi Kota Bitung.Kata Kunci :pertumbuhan ekonomi, belanja langsung, penduduk, regresi bergandaABSTRACT    The economy experienced a period of negative growth means economic activity in this period has decreased. Bitung-year period 2004-2014 economic growth fluctuations. These fluctuations can be influenced by private investment, direct spending, and population Economic growth is one measure of the success of economic development in an area. Economic growth reflects economic activity. Economic growth can be positive and can also be negative. If the economy experienced a period of positive growth means economic activity during the period has increased. Whereas if  years 2004-2014 are sourced from the Central Statistics Agency of North Sulawesi Province and Bitung. The analytical method used is an econometric model double-log regression (log-log) with Ordinary Least Square (OLS). This study aims to determine whether the development of private investment, direct spending, and population affect the economic growth of the city of Bitung. The data used using time series data period.    The results of the regression model of economic growth with the regression equation is LPDRB = - LINV 4.445 + 0.036 + 0.049 + 2.229 LPOP LBL. From these results show the development of private investment, direct expenditure and population positive and significant impact on economic growth of Bitung.Keywords: Economic growth, direct spending, population, regression.

scholarly journals City Best Practices to Improve Transit Operations and Safety

2021 ◽  
Author(s):  
Michelle DeRobertis ◽  
Christopher E. Ferrell ◽  
Richard W. Lee ◽  
David Moore
Keyword(s):  
Best Practices ◽  
Public Transportation ◽  
Land Development ◽  
Metropolitan Planning ◽  
Passenger Safety ◽  
Municipal Policies ◽  
Transit Services ◽  
Transit Agencies ◽  
The City ◽  
Policies And Practices

Public, fixed-route transit services most commonly operate on public streets. In addition, transit passengers must use sidewalks to access transit stops and stations. However, streets and sidewalks are under the jurisdiction of municipalities, not transit agencies. Various municipal policies, practices, and decisions affect transit operations, rider convenience, and passenger safety. Thus, these government entities have an important influence over the quality, safety, and convenience of transit services in their jurisdictions. This research identified municipal policies and practices that affect public transport providers’ ability to deliver transit services. They were found from a comprehensive literature review, interviews and discussions with five local transit agencies in the U.S., five public transportation experts and staff from five California cities. The city policies and practices identified fall into the following five categories: Infrastructure for buses, including bus lanes, signal treatments, curbside access; Infrastructure for pedestrians walking and bicycling to, and waiting at, transit stops and stations; Internal transportation planning policies and practices; Land development review policies; Regional and metropolitan planning organization (MPO) issues. The understanding, acknowledgment, and implementation of policies and practices identified in this report can help municipalities proactively work with local transit providers to more efficiently and effectively operate transit service and improve passenger comfort and safety on city streets.

Quantifying changes in bicycle volumes using crowdsourced data

2022 ◽  
pp. 239980832110661
Author(s):  
Ali Al-Ramini ◽  
Mohammad A Takallou ◽  
Daniel P Piatkowski ◽  
Fadi Alsaleem
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Crowdsourced Data ◽  
Machine Learning Approach ◽  
Bicycle Infrastructure ◽  
The Difference ◽  
Infrastructure Investments ◽  
Using Data ◽  
The Impact ◽  
The City

Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

Corporate innovation in a world of common ownership

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cheng-Huei Chiao ◽  
Bin Qiu ◽  
Bin Wang
Keyword(s):  
Protective Role ◽  
Least Square ◽  
Content Type ◽  
Ordinary Least Square ◽  
Corporate Innovation ◽  
Common Ownership ◽  
Regulatory Perspective ◽  
Innovation Output ◽  
The Difference ◽  
The Impact

PurposeThe purpose of this paper is to examine the impact of common ownership on corporate innovation, including innovation input, innovation output and postgrant patents.Design/methodology/approachThis paper uses the ordinary least square model and the difference-in-differences technique to evaluate the effect of institutional interlocking shareholdings on the life cycle of corporate innovation.FindingsThe results show that common ownership impedes innovation measured by patent grants and citations through reduced R&D expenditures. However, common ownership protects postgrant patents by lowering the likelihood that a co-owned firm gets involved in patent litigation and by accelerating the settlement of lawsuits between co-owned firms.Practical implicationsFrom a regulatory perspective, common ownership in younger firms that rely heavily on R&D investment to produce innovation outputs is detrimental and needs to be regulated. However, common ownership in mature firms, which hold a big pool of patents or rely on acquiring patents to compete, is of less concern because of the protective role detected.Originality/valueThe paper provides a first comprehensive look into how same-industry common ownership affects innovation input, innovation output and postgrant patents. The research also reconciles the anticompetitive effect and the coordinative effect of common ownership documented in the literature.

Cashless Payments and its Impact on Inflation

2020 ◽  
Vol 7 (9) ◽  
pp. 524-532
Author(s):  
Pisi Bethania Titalessy
Keyword(s):  
Credit Card ◽  
Least Square ◽  
Research Approach ◽  
Electronic Money ◽  
Ordinary Least Square ◽  
Cash Transaction ◽  
Speed Up ◽  
Using Data ◽  
Relationship Of ◽  
The Relationship

Payment with a non-cash system can simplify transactions and are increasingly used. The advantages of non-cash payments are not only due to convenience, speed up transaction time, and time savings but also the benefits that can reduce the circulation of money in the community. The less the amount of physical money in circulation, it will indirectly affect the inflation rate. However, there are inconsistency of research results regarding the relationship of non-cash transactions and inflation. These issues constitute a research gap on cashless payments and inflation in Indonesia. This study aims to prove the relationship between cashless payments and inflation in Indonesia. Using data from Central Bureau of Statistics Republic of Indonesia and Bank Indonesia over the period 2019-2020Q2, the results confirm that electronic money decrease inflation. The research approach in this study focuses on quantitative analysis using the Ordinary Least Square (OLS) method. The results of this study indicate that partially the relationship between debit card transactions and inflation has no significant effect. Credit card transactions have no significant effect on inflation, while electronic money transactions have a significant effect on inflation in Indonesia. Non-cash transactions intensified by Bank Indonesia through the cash-less society need to be considered more with the public's understanding of the use of non-cash transaction instruments so that the use of non-cash transactions in Indonesia is not only used for cash withdrawals but is used in every transaction.

scholarly journals KARAKTERISTIK DAN POLA PERGERAKAN PENDUDUK KOTA BATAM DAN HUBUNGANNYA DENGAN PERKEMBANGAN WILAYAH HINTERLAND

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Imam Setyohadi
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Rural Areas ◽  
Industrial Development ◽  
Coastal Region ◽  
Industrial Area ◽  
City Center ◽  
The People ◽  
The Many ◽  
The City

Batam Island’s role as an industrial development center causes fast-paced growth in many sectors with an uneven inhabitant distribution. With most people inhabit the Batam city.The hinterland area is characterised by islands and housing pattern of community clusters in the form of fishermen’s neighborhood along the coastal region with most of the houses are semi-permanent buildings. Most of people’s activities there are consisted of fishing. With limited infrastructure, the mode of transportation used in between the many islands there is with motor boats (Pancung). This is very much in contrast with the condition down the Batam City, in terms of its socio-economic, housing and other aspects.This research is aimed at figuring out the reason as to why people prefer to have their house located in rural areas and finding out the characteristic pattern of people’s movement around the Batam Island.Samples were taken using the sample random proporsional method. Samples were taken randomly from many districts with a proper ratio of people from weak, middle and upper class economic strata. The data were then analyized with cross classification.Results show that people chose rural areas because that where the industrial locations are so that it is closer for them to go to work, and the housin price or rent there are relatively cheaper too. Whereas people who chose to live in the Batam City do so because they want to improve their livelyhood by opening up alternative businesses other than working within the industry.People’s traveling destination is in and around the rural areas because it is where the industries are located evenly and the rest of them travel to the areas surrounding Batam Island. Such as people of Nongsa, 23% of the travel to the Kabil industrial area, and 12% of them go to the city center (Nagoya). In contrast, people of the Sekupang District, 29% of them travel only in and around Sekupang area. Other than that only 2% of Batam inhabitants travel to the islands surrounding Batam Island. Whereas for the people of Sei Beduk, 29% of them go to Muka Kuning and 14% of them to the city center. And for the people of Lubuk Baja District, 32% of them go to the city center (Nagoya), because it is actually the CBD center.The type of transportation used by people in rural areas are mostly private vehicles because they are thought to be more comfortable and safe, as is the case for Nongsa and Sekupang. Nonetheless, 2% of Sekupang inhabitants use motor boats to travel to the islands surrounding Batam Island. In Nongsa, 36% of the people use motorcycle, public transportation account for 32%, and in Sekupang car owners are around 35%. Contrary, the people of Sei Beduk are mostly using public transportation, 42%, due to the already availabel public road and vehicles and only 34% use cars.As for the distance covered by those people, Nongsa and Sekupang residents are 42% cover around 5-10 Km with a travel time of 20-30 minutes a day. Whereas the people of Sei Beduk, 45% of them only cover 105 km in around 10 minutes a day due to the proximity of their living place with the industrial location.On the other hand, Lubuk Baja (city center) people are 43% using cars,  for the are much more comfortable and allow them to have higher mobility. And only 31% of the inhabitants there use public transportation. Most of the people there cover 1-5 km (52%) to 5-10 km (27%) a day with a travel time of around 10 to 20-30 minutes. 

scholarly journals PENGARUH KOMPETENSI DAN KOMPENSASI TERHADAP KINERJA KARYAWAN

2020 ◽  
Vol 3 (1) ◽  
pp. 58-67
Author(s):  
Yadi Maryadi ◽  
Yulia Misrania
Keyword(s):  
Regression Coefficient ◽  
Employee Performance ◽  
Regression Equation ◽  
Least Square ◽  
Ordinary Least Square ◽  
Independent Variables ◽  
Hotel Employees ◽  
Independent Variable ◽  
The City

This study aims to determine the effect of Competence and Compensation on Employee Performance (Case Study on Hotel Employees in the city of Pagar Alam). By using primary data and secondary data, this research uses the Ordinary Least Square method. The results of this study are the findings of the influence of Compensation and Compensation on Employee Performance (Case Study on Hotel Employees in the City of Pagar Alam) can be explained as follows. Based on the Regression analysis in the table above, the constant value α = 38,868 and coefficient b1 =. 083 and b2 = .396, so that the multiple linear regression equation is: Y = 38.868+ 0.083X1 + 0.396X2. The regression equation that has been obtained can be used to predict the value of the independent variable and the dependent variable, as follows: A constant value of 38,868 means that if all independent variables are zero, then the performance of hotel clerks in the city of Pagar Alam is valued at 38,868. Competence (X1) has a regression coefficient of .083 meaning that each increase in Competency by 1 unit then the performance of hotel employees increases by .083 assuming the other factors remain. Compensation (X2) has a regression coefficient of .396 meaning that each increase in compensation is 1 unit, the performance of hotel employees increases by .396. From the determination coefficient table above, it can be seen that the R Square figure is 0.931. This means that the influence between the independent variables with the dependent variable is 93.1% while the remaining 6.9% is influenced by other factors outside the regression model analyzed. From these figures it can be concluded that the influence of the independent variable with the dependent variable is very strong.   Penelitian ini bertujuan untuk mengetahui pengaruh Kompetensi dan Kompensasi terhadap Kinerja Karyawan (Studi Kasus pada Karyawan hotel di Kota Pagar Alam). Dengan menggunakan data primer dan data skunder penelitian ini menggunakan metode Regresi Linear Sederhana (Ordinary Least Square). Adapun hasil Penelitian ini adalah Hasil temuan mengenai pengaruh Komptensi dan Kompensasi terhadap Kinerja Karyawan (Studi Kasus pada Karyawan hotel di Kota Pagar Alam) dapat dijelaskan sebagai berikut. Berdasarkan analisis Regresi pada tabel diatas didapatkan  nilai konstanta α = 38.868 dan koefisien b1 = . 083  dan b2 = .396, sehingga persamaan regresi linier berganda adalah :  Y =  38.868+ 0.083X1 + 0.396X2. Persamaan regresi yang telah diperoleh dapat dipergunakan untuk memprediksi nilai variabel independen dan variabel dependen yaitu sebagai berikut : Nilai konstanta sebesar 38.868 artinya bahwa jika semua variabel independen benilai nol, maka kinerja kayawan hotel di kota Pagar Alam bernilai sebesar 38.868. Kompetensi (X1) mempunyai koefisien regresi sebesar .083 artinya setiap kenaikan Kompetensi sebesar 1 satuan maka kinerja karyawan hotel naik sebesar .083 dengan asumsi faktor-faktor yang lain tetap. Kompensasi (X2) mempunyai koefisien regresi sebesar .396 artinya setiap kenaikan kompensasi sebesar 1 satuan maka kinerja karyawan hotel naik sebesar .396. Dari tabel koefisien determinasi di atas, dapat dilihat bahwa angka R Square sebesar 0.931. Hal ini berarti pengaruh antar variabel independen dengan variabel dependen sebesar 93,1% sedangkan sisanya sebesar 6,9% dipengaruhi oleh faktor-faktor lain di luar model regresi yang dianalisis. Dari angka tersebut dapat diambil kesimpulan bahwa pengaruh antara variabel independen dengan variabel dependen sangat kuat.

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