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This dissertation examines how people understood the phenomenon of traffic congestion in Boston in the 1890's and 1920's, tracking the evolution of their ideas between two periods. Then, as today, public discussions of policies to relieve congestion were based upon ideas about such issues as what causes congestion and why it matters. To understand how congestion was perceived in these eras, I used a case study approach, looking at discussions of it during two sets of planning debates. The first case is a debate from 1891 to 1894 that led to the building of a subway in downtown Boston. The second case is a debate in the mid-1920's over plans for the so-called "loop highway," a boulevard running through the downtown. I posed three research questions to limit and define the meaning of the term "perceptions" for this analysis: why did Bostonians think traffic congestion was a problem, what did they think caused congestion, and what policies did they think might reduce it? To answer these questions, I analyzed the words of the people involved in the debates, using materials such as newspapers, government publications, and magazines. Three themes stand out among the conclusions I drew about perceptions of congestion during the two periods. First, the factors people perceived as causing congestion were closely linked to the policies they favored. Second, most people didn't actually talk much about how they perceived congestion, even though they believed it was a problem. This relative silence reflects the fact that ideas about congestion were not particularly controversial. Third, many perceptions that Bostonians held about congestion were not only accepted as conventional wisdom within each time period, but they changed very little across the two periods - people understood congestion in the 1890's in many of the same ways that they did in the 1920's, even though traffic conditions had changed radically in the intervening years. For example, the favored policies were major capital projects; while regulatory approaches were proposed, opposition from interest groups or the public blocked the implementation of all but the most limited new rules.

This paper aims to reveal the behavior and perception of Jakarta's citizens on traffic congestion in Jakarta. Although this approach is somewhat well-developed in behavioral science, its utilization in urban economics study, is still limited. Detecting the traffic congestion and its cause mainly relies on physical (engineering) methods, i.e V/C ratio. Here, we define the traffic congestion through two variables; ordinal traffic congestion perception and proportion of expected travel time to perceived travel time. Using a non-probabilistic sampling survey held in one of densest business district in Jakarta called Sudirman-Thamrin Golden Triangle Area; the estimation results show that travel behavior plays a major role in affecting travel time perceptions.AbstrakStudi ini bertujuan untuk melihat tingkah laku masyarakat Jakarta terhadap kemacetan di Jakarta. Pendekatan yang digunakan dalam studi ini telah banyak dikembangkan dalam studi behavioral science, namun penggunaanya dalam studi ekonomi perkotaan masih terbatas. Mendeteksi tingkat kemacetan serta penyebabnya umumnya mengandalkan metode fisik seperti V/C ratio. Studi ini mendefinisikan tingkat kemacetan ke dalam dua variabel, persepsi tingkat kemacetan ordinasl serta proporsi dari ekspektasi waktu perjalanan terhadap waktu perjalanan actual. Dengan menggunakan survey non-probabilitic sampling di Sudirman-Tharim Golden Triangle Area, hasil estimasi menunjukkan bahwa perilaku perjalanan (travel behavior) berperan utama dalam mempengaruhi persepsi waktu perjalanan.Kata kunci: Tingkat Kemacetan; Waktu Perjalanan; Perilaku Perjalanan; PersepsiJEL classifications: R40; R41

Intelligent Transportation Systems (ITS) have been developed to improve the efficiency and safety of road transport by using new technologies for communication. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) are a subset of ITS widely used to solve different issues associated with transportation in cities. Road traffic congestion is still the most significant problem that causes important economic and productivity damages, as well as increasing environmental effects. This paper introduces an early traffic congestion alert system in a vehicular network, using the internet of things (IoT) and fuzzy logic, for optimizing the traffic and increasing the flow. The proposed system detects critical driving conditions, or any emergency situation blocking the road, and broadcasts remote warnings to the following vehicles. Since not all vehicles are equipped with new technologies, Liquid Crystal Display (LCD) fixed on the roads displays the alert to warn the other vehicles which have neither communication nor sensors. The system was designed with Raspberry Pi 3 Model B equipped with sensors and GPS module to emulate real-world vehicles. The results and observations collected during the experiments showed that the proposed system is able to monitor the road conditions, detect the emergency situation, and broadcast a warning message to the approaching vehicles.

We consider commuting in a congested urban area. While an efficient time-varying toll may eliminate queuing, a toll may not be politically feasible. We study the benefit of a substitute: a parking fee at the workplace. An optimal time-varying parking fee is charged at zero rate when there is queuing and eliminates queuing when the rate is non-zero. Within certain limits, inability to charge some drivers for parking does not reduce the potential welfare gain. Drivers who cannot be charged travel when there is queuing. In some cases, interaction between morning and evening commutes can be exploited to remove queuing completely.