بهره وری از محدودیت سرعت در شهرستانها: محاسبه ارزیابی تعادل عمومی فضایی

Road traffic speed limits are suggested to be associated with, e.g., changes in travel times, vehicle operating costs, accidents, noise and emissions. In this paper we analyze the impacts of speed limit policies, i.e. restricting the maximum permissible road traffic speed, on an urban economy. While most existing studies do only 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. It is shown that besides transport related effects additional economic effects may influence the overall performance of speed limit policies significantly. Driven by spatial economic effects, tightening speed limits on all roads, i.e. setting a general urban speed limit of, e.g. 30 km/h, lowers aggregate social welfare, although aggregate environmental and accident costs decline. However, setting speed limits around the city center only and not in suburban areas with access to beltways curtails negative effects on the urban economy and, in the end, may result in overall welfare gains. Therefore, our results suggest that implementing a general speed limit uniformly in the entire urban area, thus paying no attention to the spatial shape of the city and its road network, is likely to be an inadequate measure to enhance social welfare. However, restricting speed limits locally, thus focusing on the design of a ‘slow zone’, is essential and, in the end, is a more promising speed regulation policy having more likely the chance to enhance social welfare.

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 and 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 and 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 and 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 and 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 and 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.

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 the metropolitan area. The baseline simulations carried out reveal that, in total, tightening speed limits on all roads (down to 30 km/h) lowers aggregate welfare, although aggregate environmental and accident costs decline. Driven by a wide range of spatial economic effects, speed limits lower labor supply, income, consumption quantity and variety (spatial consumption possibilities) of urban residents, as well as economic activity of firms in the city. As a result, household mobility represented by commuting to work and shopping travel as well as commercial traffic are negatively affected. Nonetheless, though private travel demand decreases, daily average travel time goes up causing a reallocation of the individual time budget away from labor and leisure towards travel activities. Moreover, speed limit policies negatively affect rent levels, thus, rental revenues of landowners decline, and induce negative ‘tax interaction’ effects which erode governmental tax revenues forcing the public sector to raise further tax rates. However, as opposed to a general uniform speed limit, setting speed limits around the city center only and not in suburban areas with access to beltways curtails negative effects on the urban economy and, in the end, may result in realizing overall welfare gains. Sensitivity analyses additionally performed have shown that for the general speed limit policy as well as ‘slow zone’ policy scenario 3, strong assumptions concerning policy induced savings in, e.g., accident costs are needed to achieve welfare improvements while for the spatially restricted ‘slow zone’ policy scenario 1 even minor changes in assumptions may cause welfare to increase. Therefore, based on the simulations carried out, our results suggest that implementing a general speed limit uniformly in the entire urban area, thus paying no attention to the spatial shape of the city and its road network, is likely to be an inadequate measure to enhance social welfare. However, restricting speed limits locally, thus focusing on the design of a ‘slow zone’, is essential and, in the end, is a more promising speed regulation policy having more likely the chance to enhance social welfare. Looking at current practical speed limit regulations in German cities reveals that many cities actually make use of the possibility to slow down traffic speed locally. For example, VCD (2012) cites evidence that in 2011 in Munich a speed limit of 30 km/h is set at more than 80% of the road network while in Berlin about 75% of the road network is speed limited down to 30 km/h or even lower. According to this, our analyses suggest that current policies in such cities move in the right direction, where, however, such high regulation might be too restrictive. Keeping the structure and the various advantages of the spatial CGE approach employed in this study in mind one should, however, also be aware of the fact that it does not reach the accuracy of advanced pure traffic (micro-) simulation models in terms of modeling traffic flows (e.g. modeling the gap between a road user and his leader). Given evidence that more uniform speeds are associated with better safety (see Aarts and van Schagen, 2006, Garber and Ehrhart, 2000, Garber and Gadiraju, 1989 and Taylor et al., 2000) neglecting the reduction of dispersion in driving speeds induced by speed limiting measures thus underestimates the potential benefits of reduced accident costs we found (to the extent this effect is not included in cost rates). This implies that, in particular in regard to ‘slow zone’ scenarios 1 and 2, where negative and positive policy effects almost balance, positive effects might in the end even dominate on account of reduced accident costs along with savings in environmental costs. Implementing behavioral responses of car drivers to speed limit policies by endogenizing individual speed choice (Verhoef and Rouwendal, 2004) may also improve the accuracy of our results and also allows more comprehensive comparisons of transport policies (e.g. speed limit vs. congestion toll). It is further worth mentioning that it was beyond the scope of this paper to deal with the question of how appropriate and meaningful it is to aggregate small increments in travel time and whether small changes in travel times are noticeable for travelers at all. Neglecting small changes in travel times then could have impacts on policy implications. Further research along these lines could provide greater insights into the effects of speed limit policies, their performance in relation to alternative measures, and the robustness of the results of policy analyses concerning assumptions on the valuation of changes in travel times. Eventually, adjusting and calibrating the model to a concrete city would allow to account for specific urban features and, thus, may help local policymakers to better understand certain consequences of their traffic speed regulations. Related work is under way for the city of Hamburg (Germany).