TOPLU ULAŞIM ARAÇLARINDA ULAŞIM SÜRESİNİN TAHMİNİ

Year 2021, Volume: 4 Issue: 1, 119 - 128, 31.08.2021

Betül Boylu , Ali Boyacı

Abstract

Günümüzde toplu ulaşımda, otobüsün ulaşım süresinin tahmini, bilgiye kolayca erişebilen ve günlük aktivitelerini planladıkları gibi yolculuklarını da planlamak isteyen yolcular için oldukça önemlidir. Büyük şehirlerde otobüslerin varış süresi bazı öngörülemeyen dış faktörler nedeniyle çeşitlilik göstermektedir. Bu nedenle bu çalışma, GPS cihazları ile toplanan veriyi kullanarak, güçlü ancak sade bir Makine Öğrenmesi tekniği sunmaktadır. Teknik, geçmiş veriden öğrenerek ve hava durumu, yoğun saatler, haftanın yoğun günleri ve yıllın yoğun günleri gibi etkenleri göz önünde bulundurarak her durak aralığı için gelecek verisini tahmin eden Çoklu Doğrusal Regrasyon algoritmasını kullanmaktadır. Tekniği doğrulamak amacı ile bir simulasyon modeli oluşturulmuştur. Simulasyon modeli geçmiş verinin ortalaması ve gerçek veri ile kıyaslanarak modelin doğruluğu ölçülmüştür. Sonuçlar tahmin tekniğinin ortalama modeline göre daha iyi performans gösterdiğini ve gerçek veriye en yakın tahmini yaptığını göstermiştir.

Keywords

Ulaşım süresi tahmini, çoklu doğrusal regrasyon, makine öğrenmesi

Thanks

Dr Ali Boyacı

TRAVEL TIME PREDICTION IN PUBLIC TRANSPORTATION

Abstract

Today, travel time prediction is essential for passengers who can easily access information and want to be able to plan their journeys as well as their daily activities. Travel time varies due to some unpredictable external factors especially in big cities. Therefore this paper proposes a powerful but simple Machine Learning (ML) model by using data collected by GPS devices. The model uses a Multiple Linear Regression algorithm that learns from historic data and predicts future data for each bus stop interval by considering external factors such as; weather condition, peak hours, busy week days and busy days of year. A simulation model was developed to validate the model. Then the simulation model was compared to average of historic data and real data. Results show that the prediction model outperforms the average model and calculates closest travel times to the real data.

Keywords

Travel time prediction, Multiple linear regression, machine learning

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