Carlo Ciccarelli (p), Peter Groote

The recent Ciccarelli and Groote (2017) presented new historical annual estimates of the extension of the railway network in Italian provinces (NUTS 3) during the period 1839-1913. The database was built using historical and geographical sources, including a unique series of homogeneous historical maps that were scanned and georeferenced in an ArcGIS project.

The present research makes a step forward and examines how the early diffusion of the Italian railway network influenced the centrality of Italian urban nodes. Our approach can be summarized as follows. We first computed an Origin-Destination (O-D) matrix for the 69 provincial capital cities of the time assuming that all travel took place over railways (if present) or by foot (if no railway line was present). The O-D matrices were computed for benchmark years during 1839-1913.

For each (Origin) city the sum of all travel times to all other cites was calculated (so we summed the rows of the matrix). This gave a centrality measure for each city for each year. Finally, we mapped the centrality scores in ArcGIS, and spatially interpolated and extrapolated centrality between these provincial capitals using Empirical Bayesian kriging (EBK). This resulted in a centrality landscape covering the whole of Italy (at 1913 borders) for each year.

The impedances (relative costs) used in the calculation have been calculated as the minutes it would take to traversing a line, so as length divided by assumed average speed. This calculation was done in the ArcGIS field calculator.

For each year (1839-1913) an average speed for both main railways and light railways was assumed. For main railways: based on the figures for 1855, 1861,1880 and 1889 given by Albert Schram (1997), Railways and the Formation of the Italian State in the Nineteenth Century (Cambridge Studies in Italian History and Culture), p.149. Years before 1855, after 1889, and between the years mentioned have been linearly extrapolated respectively interpolated. For light railways: assumed to have been two-thirds of the average speed on main railways For connections between cities where no rail connection existed: as the Euclidean distance (‘as the crow flies’) between them traversed at walking speed (5 km/h).

Future research will account for sea transport, which seems crucial in 19th century Italy, and will correct assumed speeds for terrain differences, using height profiles of the railway lines estimated in ArcGIS using elevation layers.

Taede Tillema (p), George Gelauff, Jan van der Waard

A future in which self-driving cars determine the traffic situation, how does it look like and when can we expect it – either on highways or everywhere inside and outside urban areas? And what may be the long-term spatial consequences of self-driving vehicles? These questions are surrounded by many uncertainties. As technological developments are successful, the technology is affordable, motorists favour such developments and the societal effects are positive, a self-driving future may very well be possible. Policies may also accelerate such transition to a self-driving future. At the same time there may be 'showstoppers': developments that may slow down or hamper the transition.

Within this paper we use four earlier developed scenarios of self-driving vehicles and the implications for the traffic and transport system of the future as input for a backcasting process which results in two transition pathways towards a traffic and transport system with self-driving vehicles: “Evolution of the private car”, and, “Sharing in bloom. “Evolution of the private car” describes a transition in which most people still like to own their own vehicle. The technological possibilities of self-driving vehicles increase gradually and penetrate the market step-by-step. In the transition pathway “Sharing in bloom” car and ride sharing is self-evident. The speed of the transition in both paths is one of the major uncertainties. That will depend on technological developments, but also on how fast self-driving vehicles actually reach the market and whether they resonate with the public.

On both transition paths, there are five steps in which a combination of developments can strongly influence the transition: 1) The interaction between man and machine; 2) cooperative or autonomous driving; 3) mixed traffic on the highway; 4) The dilemma whether or not to separate traffic flows/modalities within urban areas; 5) “the self-driving city”.

We map out concrete points of engagement for transport and spatial policy for each of these transition stages, which are then divided into four categories: regulation and coordination; facilitation, execution and experimentation; conducting research and monitoring /evaluation.

Mariko Futamura, Takamune Fujii (p)

Japan is an Island country, with many small isolated islands. According to Japan Coast Guard, there are 6,852 islands in Japan, and 305 islands of them have resident population. For the inhabitants of those small and isolated islands, regular lines to main 5 islands in Japan (Hokkaido, Honshu, Shikoku, Kyushu and Okinawa islands) are crucial for their living standard or logistics of daily goods like fuel and water. However, in recent years, many of these islands have problems of aging and decreasing population and so it has been difficult to manage and keep profitable the regular line for these small islands. Because of these hard business situations, number of regular shipping line for remote islands have been decreasing. According to the Ministry of Land, Infrastructure, Transportation and Tourism, GOJ, there are 235 shipping operators provided 288 regular lines in 2014, even though there are 300 lines in 2011. Then, most of those lines is provided by small shipping operators and they have difficulty to sustain these lines. Because of the situations, Japanese government (including local government) supports these operators by granting subsidy. In this paper, we examine what are the optimal political and institutional measures for the problem of sustaining regular lines for remote islands by the econometric analysis. And we try to have some implication for improving profitability of these liner operators.