Efficiency of speed limits in cities: A spatial computable general equilibrium assessment
Introduction
A major issue surrounding the effects of tightening road traffic speed limits in urban areas concerns the impacts on mobility and the environment. Speed limit policies – either already implemented or at least controversially discussed in cities or countries around the world – are suggested to be associated with, e.g., changes in travel times, congestion levels, vehicle operating costs, the frequency and severity of accidents, noise and emissions of air pollutants and carbon dioxide. Furthermore, because car drivers seem to overestimate time benefits from speeding at the expense of higher accident risks (see e.g. Elvik, 2010, Matsuki et al., 2002), only consider private costs (ignore externalities) by their choice of driving speed, and are just inadequately informed on traffic conditions and their consequences, regulating drivers speed choice may be a useful and essential traffic managing instrument (see e.g. Archer et al., 2008).
The suggested positive impacts of speed limits have triggered European citizens to form an initiative called “30 km/h – making the streets liveable!”.1 The ‘vision’ of the initiative is that a car speed of 30 km/h should no longer be limited to single zones, but shall become the standard speed limit for villages, towns and cities with local authorities being able to decide on exemptions. To meet the subsidiarity principle, the local authorities should have the final decision to set other speed limits on their roads and implement equivalent alternatives to meet, e.g., environment related goals.
There are extensive research efforts towards the impacts of lowered automobile travel speed on accidents, CO2 emissions, noise and air pollution. In particular the relationship between driving speed and the risk and severity of road crashes has been analyzed and reviewed to a large extent (see e.g. Aarts and van Schagen, 2006, Aljanahi et al., 1999, Archer et al., 2008, Baruya and Finch, 1994, BMJ, 2009, Elvik, 2009, Elvik and Amundsen, 2000, Elvik et al., 2004, Garber and Graham, 1990, Joksch, 1975, Kloeden et al., 1997, Kloeden et al., 2001, Kloeden et al., 2002, Lai et al., 2012, Nilsson, 1982, Nilsson, 2004, OECD/ECMT, 2006, Taylor et al., 2000, Wong et al., 2005). Some studies figured out an evidence for an exponential function or a power function between speed and accidents/crash rates. But almost all studies conclude that the probability of being involved in a crash as well as the severity of an accident increases with travel speed and that lowering speeds improves the interaction between different road users.2 Furthermore, there is evidence that increasing speed differences between vehicles (speed dispersion) increase the crash rate, too.3
The impact of speed management policies on CO2 and air pollution emissions are analyzed in detail as well (see e.g. Baldasano et al., 2010, Dijkema et al., 2008, Gan et al., 2012, Madireddy et al., 2011, Int Panis et al., 2011, Int Panis et al., 2006, OECD/ECMT, 2006, Owen, 2005). These studies show that reducing speed on urban ring highways/beltways significantly reduces emissions. For local urban roads, however, this picture is less clear.4
Studies examining the impact of reduced speeds on noise emissions (see e.g. Amundsen and Klæboe, 2005, den Boer and Schroton, 2007, Dora et al., 2011, Freitas et al., 2012, Gan et al., 2012, Nijland and Van Wee, 2012, OECD/ECMT, 2006) mainly conclude that lowering speeds reduces noise emissions,5 but the potential of noise reduction is mainly influenced by the speed level.
Further studies analyze the impacts on speed choice behavior (see e.g. Åberg et al., 1997, Delhomme et al., 2010, Elvik, 2010, Elvik, 2009, Fuller et al., 2009, Haglund and Åberg, 2000, Matsuki et al., 2002, Nilsson, 1991, Schmid Mast et al., 2008, Tarko, 2009). Their main results can be summarized as follows: first, most drivers choose speed above the limit because they overestimate time profits as well as they underestimate rising accident risks from speeding6; second, because drivers experience social pressure from other road users they choose their speed according to the speed of others even though the speed is above the limit (Åberg et al., 1997); and third, although drivers are aware of the negative impact of speed on noise and emissions, this knowledge affects the choice of speed only to a little degree.7
Considering the different research topics reveals that analyses regarding road traffic speed limits mainly focus on environmental effects and accidents caused by adjustments in transport. However, in addition to pure transport related effects particularly on accidents or emissions, speed limits may have various further impacts from an economic perspective. Because urban areas constitute economies on a local scale, additional economic effects arising in cities inhabited by workers or consumers facing the necessity of being mobile, occupied by businesses and firms which are reliant on commercial traffic, and governed by local/federal authorities imposing, collecting, and redistributing fees and taxes could influence the performance of speed limits to a similar degree or even more.
The objective of this paper is therefore to provide a more general assessment of speed limit policies by examining their overall impacts on a metropolitan area and its residents. The overall assessment includes environmental, safety, economic, transport related and spatial effects. In order to account for several interdependencies between economic agents (households, firms, the public sector) and their decisions on urban markets we develop and employ a spatial computable general equilibrium model (CGE) that takes into account the endogeneity of location decisions of households and firms, endogenous labor-leisure choice, traffic congestion, fuel consumption, traffic related CO2 emissions concerning private and commercial traffic, travel mode and route choice. In addition, the presence of multiple distortionary taxes allows to account for feedback effects on endogenous governmental tax revenues. All these features are essential to study the impacts of speed limits on cities and, to the best of our knowledge, have never been considered simultaneously in speed limit policy analyses.
We consider differentiated speed limiting measures, i.e. either restricting speeds on all roads (local city roads and bypassing beltways) or setting speed limits on local roads around the city center only. Based on the effects of reduced travel speeds reported in the literature, environmental and accident costs can be expected to be reduced. Imposing a speed limit is likely to make traveling more expensive accounting for time values which might cause adjustment in individual behavior with respect to travel demand (trip distance and/or frequency), travel mode and/or route choice. These behavioral adjustments, in turn, may result in changes in emission and accident costs. In addition to these travel related effects, changes in relative prices might be able to drive spatial economic effects. Tightening speed limits then may also affect leisure demand/labor supply and associated with it the allocation of individual time budgets towards less travel activities, income, commodity demand, spatial consumption possibilities and location decisions of urban residents as well as economic activity of firms in the city by affecting freight traffic. Moreover, behavioral changes of residents might have impacts on governmental tax revenues via tax interaction effect and on rent dividend income of landowners working via the urban land/housing market. Consequently, additional spatial economic effects are likely to constitute a countervailing force to the potential welfare enhancement caused by a reduction in environmental and accident costs. The spatial CGE simulations carried out will reveal the importance of these differentiated effects and thus, the potential of the speed limit policies to enhance social welfare.
The remainder of the paper is organized as follows: Section 2 presents the main mathematical formulations of the spatial urban general equilibrium model and describes its main characteristics. In Section 3 we give a description of the model calibration according to German data. Furthermore, we present some results of the initial benchmark simulation and their correlation with empirical and statistical evidence. Section 4 explains the way speed limits are implemented as well as the scenario design of the speed limit policies and, subsequently, presents and analyzes the results of the simulations including sensitivity analyses. Eventually, Section 5 concludes.
Section snippets
The general setting
Our calculations are generally based on the spatial urban general equilibrium model originally developed by Anas and Kim, 1996, Anas and Xu, 1999, Anas and Rhee, 2006. Here we employ the modified version of Tscharaktschiew and Hirte, 2010a, Tscharaktschiew and Hirte, 2010b, Tscharaktschiew and Hirte, 2012 extended, first, by the transportation part of the RELU-TRAN8 algorithm described in Anas and Liu (2007)
Model calibration and benchmark
The spatial urban general equilibrium model was calibrated to reproduce economic, spatial and traffic related conditions of an ‘average’ German metropolitan area. In doing so some parameters were fixed at empirically observable values and others were calibrated to fit features of the benchmark city to empirical German characteristics. In particular
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economic characteristics such as wage and income,29
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Results of the speed reduction policies
In the following we present the simulation results carried out for the different speed reduction policy scenarios. In particular we will describe overall welfare implications of the various policies. Welfare effects are a combination of behavioral changes (e.g. route choice, travel mode choice, adjustments in time allocation, changes in demand patterns) economic, environmental and spatial effects which will be discussed in more detail below.
Welfare changes of urban households and landowners are
Conclusions
In this paper we have analyzed the impacts of speed limit policies on an urban economy. While most existing studies focus on the effects of speed limits on frequency and severity of accidents, we provide a more general assessment of speed limit policies by employing a spatial computable general equilibrium model calibrated to an ‘average’ German metropolitan area.
Based on the benchmark urban economy, we studied several scenarios all restricting the maximum permissible road traffic speed within
Acknowledgements
This paper has been written within the framework of the project “Evaluating Measures on Climate Protection and Adaptation to Climate Change in Agglomerations (EMPACCA)” which is part of the program: “Economics of Climate Change”. Funds from the German Ministry of Education and Research (BMBF) are gratefully acknowledged. We also wish to thank Hyok-Joo Rhee and three anonymous referees for interesting comments and helpful suggestions on an earlier draft of this paper. Eventually, a special thank
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