Origin-destination matrix generation using smart card data: Case study for Izmir

Abstract

In parallel with growing populations and the development of the cities, the traffic densities and respectively the need for public transportation increase. While the local authorities transport the passengers to most of the locations they want, they must also maintain a high-quality public transport system to keep passengers' satisfaction levels high. The most reliable way to conduct such a good transport planning study is to benefit from actual numerical data. The number of the studies related to public transport planning by using smart card data has increased along with the developing technology and widespread usage of electronic fare collection systems. In Izmir city, which has employed an electronic fare collection system since 1999, a fully integrated public transport system has been developed that includes bus, light rail system, metro and ferry modes. In this study, the stages of constructing an origin-destination (OD) matrix that shows the regions where passengers board and alight are evaluated, and an example OD matrix generated by using actual data is interpreted.

Otobüs İçi Yoğunluk Oranını Dikkate Alan Bulanık Optimal Güzergah Öneri Modeli ve Çözüm Algoritması

“…Nasibov ve ark. [6] ve Diker [10] çalışmalarında İzmir BB ESHOT Genel Müdürlüğü verilerine göre yolculuk zinciri yöntemi kullanılarak iniş tahminleri yapılmıştır. Bu çalışmada ise, biniş ve iniş bilgileri kullanılarak her hatta yer alan sefer bazlı ortalama otobüs içi yoğunluk bilgileri hesaplanmaktadır.…”

“…Nasibov ve ark. [6] çalışmasında İzmir kenti için akıllı ulaşım kartı verileri bazında OD matrisi hesaplanmışlardır. Munizaga ve Palma [7] çalışmasında, Santiago, Şili'deki büyük ulaşım ağı için hem akıllı kart hem de GPS verilerinden OD matrisini tahmin etmek için bir metodoloji sunmuştur.…”

Intermunicipal Travel Networks of Mexico (2020-2021)

“…mobility patters include large-scale and long-range commuting patterns [20] [36]; spatio-temporal patterns for different socioeconomic strata [25]; patterns in bike-sharing systems [24], etc. Origin-destination matrices have also been used to measure the volume of use of public transport [2][44] [28] and public transport planning [29] [1].…”